Context indicators1

Indicator No. Indicator name

Socio-economic indicators

C.01 Population

C.02 Age structure

C.03 Territory

C.04 Population density

C.05 Employment rate (*)

C.06 Self-employment rate

C.07 Unemployment rate

C.08 GDP per capita (*)

C.09 Poverty rate (*)

C.10 Structure of the economy

C.11 Structure of the employment

C.12 Labour productivity by economic sector

Sectorial indicators

C.13 Employment by economic activity

C.14 Labour productivity in agriculture

C.15 Labour productivity in forestry

C.16 Labour productivity in the food industry

C.17 Agricultural holdings (farms)

C.18 Agricultural area

C.19 Agricultural area under organic farming

C.20 Irrigated land

C.21 Livestock units

C.22 Farm labour force

C.23 Age structure of farm managers

C.24 Agricultural training of farm managers

C.25 Agricultural factor income (*)

C.26 Agricultural entrepreneurial income (*)

C.27 Total factor productivity in agriculture (*)

1 COMMISSION IMPLEMENTING REGULATION (EU) No 834/2014 of 22 July 2014 laying down rules for the

application of the common monitoring and evaluation framework of the common agricultural policy and COMMISSION IMPLEMENTING REGULATION (EU) No 808/2014 of 17 July 2014 laying down rules for the application of Regulation (EU) No 1305/2013 of the European Parliament and of the Council on support for rural development by the European Agricultural Fund for Rural Development (EAFRD).

2

C.28 Gross fixed capital formation in agriculture

C.29 Forest and other wooded land (FOWL)

C.30 Tourism infrastructure

Environment indicators

C.31 Land cover

C.32 Areas facing natural and other specific constraints (ANCs)2

C.33 Farming intensity

C.34 Natura 2000 areas

C.35 Farmland birds index (FBI) (*)

C.36 Conservation status of agricultural habitats (grassland)

C.37 HNV (high nature value) farming (*)

C.38 Protected forest

C.39 Water abstraction in agriculture (*)

C.40 Water quality (*)

C.41 Soil organic matter in arable land (*)

C.42 Soil erosion by water (*)

C.43 Production of renewable energy from agriculture and forestry

C.44 Energy use in agriculture, forestry and food industry

C.45 Emissions from agriculture (*)

Context indicators which incorporate CAP impact indicators are marked with an asterisk (*).

2 According to the COMMISSION IMPLEMENTING REGULATION (EU) No 808/2014 this indicator is called

"Less-favoured areas"

3

FICHE CONTENTS

Indicator Name Title of the indicator used in the commission implementing

regulation/guidance documents

Type of indicator Socio-economic / sectorial / environmental indicator

Definition Concise definition of the concept, including if the indicator already exists,

e.g. Agri-environmental indicator (AEI), EUROSTAT indicator. If appropriate,

include the methodology/formula for establishment of the indicator

Unit of measurement Unit used to record the value (e.g. ha, tonnes, €, %)

Methodology/formula Identification of what is needed to transform data from the operation

database into value for the indicator

Data source Identification of existing data sources (e.g. EUROSTAT identifying relevant

data set, Farm Accountancy Data Network (FADN), European Environmental

Agency, etc.)

References/location of

the data

Links (other references) to data sources (e.g. in EUROSTAT specifying exact

tables, FAO, World bank) AEI definitions, regulations establishing indicators,

etc.

Data collection level Identification of the geographical level at which the data is available and at

which level the indicator should be established

Frequency Frequency at which the indicator is collected/calculated

Delay How old are the data when they become available

Comments/caveats Comments concerning interpretation of the indicator for monitoring and

evaluation purposes and its caveats, if appropriate

4

INDICATOR C.01

Indicator Name Population

Type of indicator Socio-economic

Definition This indicator refers to the population on 1st of January of any given year: total and by type of region (predominantly rural, intermediate and predominantly urban).

Unit of

measurement

Total population:

- inhabitants (in thousands)

In each type of region:

- % of total population

Data source Eurostat – population statistics

Eurostat – regional statistics by urban-rural typology

References/location

of the data

National data: Table Population change - Demographic balance and crude rates at

national level [demo_gind]

Regional data: table Population change - Demographic balance and crude rates at

regional level (NUTS 3) [demo_r_gind3]

National data, by typology: table Demographic balance and crude rates [urt_gind3]

Regional data, by typology: Directorate-General for Agriculture and Rural

Development (DG AGRI) calculation using regional data

Data collection level EU, national (NUTS 0) and regional (NUTS 1, 2 and 3)

Frequency Annual

Delay 1 year

Comments/caveats

The distribution of population by type of region has been calculated using the

Commission's urban-rural typology, which classifies NUTS 3 regions into

predominantly rural, intermediate and predominantly urban.

5

INDICATOR C.02

Indicator Name Age structure

Type of indicator Socio-economic

Definition

This indicator refers to the age structure of the EU population on 1st of January of any given year by broad age groups (less than 15 years / from 15 to 64 years / 65 years or over), total and by type of region (predominantly rural, intermediate and predominantly urban).

The population is based on data from the most recent population census, adjusted by the components of population change produced since the last census, or based on population registers.

In case of missing data for some regions or Member States, the population of the year in question has been estimated based on the population of the previous available year.

Unit of

measurement

Total and in each type of region:

- persons in each age group

- % of total population

Data source Eurostat - population statistics

Eurostat - regional statistics by urban-rural typology

References/location

of the data

National data: Table Population on 1 January by broad age group and sex

[demo_pjanbroad]

Regional data: Table Population on 1 January by broad age group, sex and NUTS 3

region [demo_r_pjanaggr3]

National data, by typology: Table Population by sex and age groups on 1 January

[urt_pjanaggr3] (see comments)

Regional data, by typology: DG AGRI calculation using regional data

Data collection level EU, national (NUTS 0) and regional (NUTS 1, 2 and 3)

Frequency Annual

Delay 1 year

Comments/caveats

The age structure by type of region has been calculated using the Commission's

urban-rural typology, which classifies NUTS 3 regions into predominantly rural,

intermediate and predominantly urban.

6

INDICATOR C.03

Indicator Name Territory

Type of indicator Socio-economic

Definition

This indicator refers to the total area (including inland waters) and the distribution by type of region (predominantly rural, intermediate and predominantly urban).

It provides data both in absolute values (for the total EU territory) and as a share of the total area (for the data by type of region).

Unit of

measurement

Total area:

- km2

In each type of region:

- % of total area

Data source Eurostat - population statistics

Eurostat - regional statistics by urban-rural typology

References/location

of the data

National data: DG AGRI calculation using regional data (no table available in Eurostat)

Regional data: Table: Area by NUTS 3 region [demo_r_d3area]

Regional data, by typology: DG AGRI calculation using regional data

Data collection level EU, national (NUTS 0)and regional (NUTS 1, 2 and 3)

Frequency Annual

Delay 1 year

Comments/caveats

In case of missing data, land area has been used instead of total area.

The distribution of territory by type of region has been calculated using the

Commission's urban-rural typology, which classifies NUTS 3 regions into

predominantly rural, intermediate and predominantly urban. Missing data on "Total

area" are completed with data on "Land area" of data source [demo_r_d3area].

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INDICATOR C.04

Indicator Name Population density

Type of indicator Socio-economic

Definition

This indicator is calculated as the annual average population / land area and refers to the number of inhabitants per km2.

It is provided at total level (national/regional) and for the predominantly rural, intermediate and urban regions.

Unit of

measurement

Total

- Inhabitants/km2

In each type of region:

- Inhabitants/km2

Data source

Eurostat – population statistics

Eurostat – regional demographic statistics

Eurostat – regional statistics by typology

References/location

of the data

National data: Table: Population density calculation from demo_r_d3dens

Regional data: Table: Population density by NUTS 3 region [demo_r_d3dens] or, in

case of missing data:

National and regional data by type of region calculated as demo_r_d3avg /

demo_r_d3area and classified according to the Commission's urban-rural typology.

Data collection level EU, national (NUTS 0) and regional (NUTS 1, 2 and 3)

Frequency Annual

Delay 2 years

Comments/caveats

The distribution of population by type of region has been calculated using the

Commission's urban-rural typology, which classifies NUTS 3 regions into

predominantly rural, intermediate and predominantly urban.

Total area (including inland waters) is used when land area is not available.

Due to the change from NUTS 2010 to NUTS 2013 in the regional classification, there

are new regions (partly new naming, partly new area delineations) which lack of data

for 2014. Germany, France, Portugal and the United Kingdom are affected. Therefore

for these Member States the national totals do not correspond to the sum of regional

totals. It should be considered when interpreting 2014 data.

8

INDICATOR C.05

Indicator Name Employment rate

Type of indicator Socio-economic

Definition

Employed persons aged 15-64 years and 20-64 years3 as a share of the total

population of the same age group in thinly populated areas (used as proxy for rural

areas):

Employed persons are all persons aged 15-20 years and over who, during the

reference week, worked at least one hour for pay or profit or were temporarily

absent from such work. Employed persons comprise employees, self-employed and

family workers.

Population covers persons aged 15-20 years and over living in private households.

This comprises all persons living in the households surveyed during the reference

week. This definition also includes persons absent from the households for short

periods (but having retained a link with the private household) owing to studies,

holidays, illness, business trips, etc. Persons on compulsory military service are not

included.

Methodology: The rural employment rate is calculated at national level using Labour

Force Survey (LFS) data aggregated by degree of urbanisation. This degree of

urbanisation classifies the territory (Local Administrative Units (LAU)) into rural areas,

towns and suburbs and cities. The rural employment rate of each Member State

would then correspond to the employment rate of rural areas; this rate could be

compared with the employment rates in the other two types of areas or with the

employment rate for the whole country. Additionally, employment rates could also be

calculated for men and women and even for other age groups, if needed for a better

analysis.

Unit of

measurement

Total and in each type of area:

- % of total population of the same age class and sex

Data source Eurostat – Labour Force Survey

Eurostat – Degree of urbanisation

References/location

of the data

National data: table lfsi_emp_a

Regional data: table lfst_r_lfe2emprt

National data, by degree of urbanisation: table lfsa_pgauws (DG AGRI calculation)

Regional data, by degree of urbanisation: tables lfst_r_lfe2emp and lfst_r_lfsd2pop

Data collection level LFS data are collected at LAU level (LAU2), with a sample defined to be significant at NUTS 2 level and at national level.

3 In the programming period 2007-2013, the employment rate was calculated for the age group of 15-64 years. In

the Europe 2020 strategy, reaching an employment rate of 75% of the population aged 20-64 years is one of the five headline targets to be achieved; however, in rural areas the employment of people below 20 is also an important indicator. Thus it is proposed to keep both age groups, which is also Eurostat's approach.

9

Frequency

For the LFS: annually, in the second half of the year.

For the aggregates by degree of urbanisation: depending on the availability of the new data.

Delay 1 year

Comments/caveats

Although the use of the degree of urbanisation has been selected as the most

appropriate for the indicator "rural employment rate", the urban/rural typology is the

one to be used when the information is available at NUTS level 3 (for example, for the

indicator "Rural GDP per capita").

Employment rates by degree of urbanisation have been calculated by DG AGRI using

the variables 'Employed persons' and 'Population' from the table lfsa_pgauws.

A change in the methodology to classify local areas from 2012 onwards has produced

a break in Eurostat series by type of area. In order to show the evolution of the

employment rates, rates from 2012 to 2015 have been recalculated using the

previous classification. EError! Reference source not found.mployment rates for the

current year are calculated by Eurostat using the current classification of areas.

10

INDICATOR C.06

Indicator Name Self-employment rate

Type of indicator Socio-economic

Definition

This indicator refers to the share of self-employed persons in total employed persons for the age class 15-64 years.

It is calculated by dividing the number of self-employed persons by the number of employed persons of a certain Member State/region.

Unit of

measurement

% of self-employed persons aged 15-64 years in total employed persons of the same

age class.

Data source Eurostat – Labour Force Survey

References/location

of the data

National data: tables lfsa_egaps / lfsa_pgauws

Regional data: tables lfst_r_lfe2estat / lfst_r_lfe2emp

Data collection level EU, national (NUTS 0) and regional (NUTS 1 and 2)

Frequency Annual

Delay 1 year

Comments/caveats

11

INDICATOR C.07

Indicator Name Unemployment rate

Type of indicator Socio-economic

Definition

This indicator provides the number of unemployed persons aged 15-24 years (youth unemployment rate) and 15-74 years (total unemployment rate) as a share of the total economically active population of the same age class: total and in the thinly-populated areas.

Unemployed persons comprise persons who were (all three conditions must be fulfilled simultaneously):

1. without work during the reference week,

2. available for work at the time,

3. actively seeking work.

Economically active population is employed plus unemployed.

Unit of

measurement

Total and in the thinly-populated areas:

- % of total active population of the same age class

Data source Eurostat – Labour Force Survey

Eurostat – Degree of urbanisation

References/location

of the data

National data, by degree of urbanisation: table lfsa_pgauws (DG AGRI calculation, see

comments)

Regional data (NUTS 1 and 2): table lfst_r_lfu3rt

Regional data, by degree of urbanisation: tables lfst_r_lfu3pers and lfst_r_lfp2act

Data collection level EU, national (NUTS 0) and regional (NUTS 1 and 2)

Frequency Annual

Delay 1 year

Comments/caveats

For the unemployment rate by type of area, the degree of urbanisation classification, which classifies Local Administrative Units (LAU2) into thinly-populated areas (= rural), intermediate urbanised areas and densely-populated areas, will be used.

Unemployment rates by degree of urbanisation have been calculated by DG AGRI using the variables ‘Unemployed persons’ and ‘Active population’ from the table lfsa_pgauws.

A change in the methodology to classify local areas from year 2012 has produced a

break in Eurostat series by type of area. In order to show the evolution of the

unemployment rates, 2012 and 2013 rates have been recalculated using the previous

classification. Error! Reference source not found.unemployment rates for the current

year are calculated by Eurostat using the current classification of areas.

For the regional data, the age class is 15 years or over.

12

INDICATOR C.08

Indicator Name GDP per capita

Type of indicator Socio-economic

Definition

Gross Domestic Product (GDP) per capita in predominantly rural regions, in

Purchasing Power Standard (PPS)4

The PPS per inhabitant in rural areas can be compared to the PPS per inhabitant at

national level (without distinction by type of region) or to other aggregations (EU-15,

EU-N13).

In particular, the following indicators are calculated by Eurostat:

PPS per inhabitant in rural, intermediate and urban areas

PPS per inhabitant in percentage of the EU average for rural, intermediate and urban areas.

Unit of

measurement

PPS (for the simple reporting of absolute values)

% (for comparison of values from rural areas to those of other areas or to the EU

average)

Data source Eurostat – National and Regional Economic Accounts

Eurostat - Rural development statistics

References/location

of the data

National data: table nama_10_gdp, demo_gind

Regional data: table nama_10r_3popgdp, nama_10r_3gdp

National and regional data, by urban-rural typology: DG AGRI calculation using

regional data

Data collection level EU, national (NUTS 0) and regional (NUTS 1, 2 and 3)

Frequency Annual

Delay 1 year (national data) and 3 years (regional data)

Comments/caveats

As an average, this indicator does not measure the distribution of income within a

given geographical area. Furthermore, non-monetary exchanges (production for self-

consumption; public goods and externalities; barter; unpaid family labour) are not

taken into account but can be substantial in some sectors (especially in agriculture)

and regions.

4 The Purchasing Power Standard, abbreviated as PPS, is an artificial currency unit. Theoretically, one PPS can buy

the same amount of goods and services in each country. However, price differences across borders mean that different amounts of national currency units are needed for the same goods and services depending on the country. PPS are derived by dividing any economic aggregate of a country in national currency by its respective Purchasing Power Parities.

13

INDICATOR C.09

Indicator Name Poverty rate

Type of indicator Socio-economic

Definition

The indicator is defined as the share of population at risk of poverty or social

exclusion in thinly populated areas (used as proxy for rural areas). It is calculated as

the percentage of people who are at risk of poverty or severely deprived or living in a

household with low work intensity over the total population.

The at-risk-of-poverty rate is the share of people with an equivalised disposable income (after social transfer) below the at-risk-of-poverty threshold, which is set at 60 % of the national median equivalised disposable income after social transfers. (http://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:At-risk-of-poverty_rate)

The degree of rural poverty (share of population at risk of poverty) can be compared

to the overall EU-27/28 average, to the respective national average and/or to the

average for intermediate and/or urban areas in a Member State or in the EU-27/28

(choice to be made according to the policy objective).

Unit of

measurement

Total and in the thinly-populated areas:

- % of total population

Data source Eurostat – Survey on income and living conditions (SILC)

Eurostat – Degree of urbanisation

References/location

of the data

National data: table ilc_peps01

Regional data: table ilc_peps11 (regional data are not available for some MS)

National data, by degree of urbanisation: table ilc_peps13

Regional data, by degree of urbanisation are not available (see comments)

Data collection level EU, national (NUTS 0) and regional (NUTS 1 and 2)

Frequency Annual

Delay 1 years

Comments/caveats

The indicator is available by degree of urbanisation:

1. Densely populated area (at least 500 inhabitants/km2)

2. Intermediate urbanised area (between 100 and 499 inhabitants/km2)

3. Thinly populated area (fewer than 100 inhabitants/km2).

To calculate the indicator, it can be assumed that thinly populated areas roughly

correspond to rural areas.

14

INDICATOR C.10

Indicator Name Structure of the economy

Type of indicator Socio-economic

Definition

Total Gross Value Added (GVA) (at basic prices) and distribution by sector (primary, secondary, tertiary) and by type of region (predominantly rural, intermediate and predominantly urban).

GVA is defined as the value of output less the value of intermediate consumption.

Output is valued at basic prices, GVA is valued at basic prices and intermediate consumption is valued at purchasers’ prices.

Unit of

measurement

Total GVA:

- EUR million

For each sector:

- % of total GVA

In each type of region:

- % of total GVA

Data source Eurostat – National and Regional Economic Accounts

References/location

of the data

National data: table nama_10_a10

Regional data: table nama_10r_3empers

National and regional data, by typology: DG AGRI calculation using regional data

Data collection level EU, national (NUTS 0) and regional (NUTS 1, 2 and 3)

Frequency Annual

Delay 1 year (national data) and 3 years (regional data)

Comments/caveats

Sectors in NACE rev.2:

Primary sector = branch A (agriculture, forestry and fishing);

Secondary sector = branches B-E + F (industry + construction);

Tertiary sector = branches G-I + J + K + L + M-N + O-Q + R-U.

The distribution of GVA by type of region has been calculated using the Commission's

urban-rural typology, which classifies NUTS 3 regions into predominantly rural,

intermediate and predominantly urban.

15

INDICATOR C.11

Indicator Name Structure of employment

Type of indicator Socio-economic

Definition This indicator shows total employment and distribution by sector (primary, secondary, tertiary) and by type of region (predominantly rural, intermediate and predominantly urban).

Unit of

measurement

Total employment:

- 1000 persons

For each sector:

- % of total employment

In each type of region:

- % of total employment

Data source Eurostat – National and Regional Economic Accounts

References/location

of the data

National data: table nama_nace10_e

Regional data: table nama_r_e3em95r2

National and regional data, by typology: DG AGRI calculation using regional data

Data collection level EU, national (NUTS 0) and regional (NUTS 1, 2 and 3)

Frequency Annual

Delay 1 year (national data) and 3 years (regional data)

Comments/caveats

Sectors in NACE rev.2:

Primary sector = branch A (agriculture, forestry and fishing);

Secondary sector = branches B-E + F (industry + construction);

Tertiary sector = branches G-I + J + K + L + M-N + O-Q + R-U.

The distribution of employment by type of region has been calculated using the

Commission's urban-rural typology, which classifies NUTS 3 regions into

predominantly rural, intermediate and predominantly urban.

16

INDICATOR C.12

Indicator Name Labour productivity by economic sector

Type of indicator Socio-economic

Definition

This indicator calculates the Gross Value Added (GVA) per employed person, total and by sector (primary, secondary, tertiary) and by type of region (predominantly rural, intermediate and predominantly urban).

GVA is calculated at basic prices. Dividing this value by the number of employed persons allows for a comparison of labour productivity over the different sectors and regions.

Unit of

measurement

Total, by branch and by type of region:

EUR/person

Data source Eurostat – National and Regional Economic Accounts

References/location

of the data

National and regional data: DG AGRI calculation using national and regional data from

C.10 and C.11

Data collection level EU, national (NUTS 0) and regional (NUTS 1, 2 and 3)

Frequency Annual

Delay 1 year (national data) and 3 years (regional data)

Comments/caveats

Sectors in NACE rev.2:

Primary sector = branch A (agriculture, forestry and fishing);

Secondary sector = branches B-E + F (industry + construction);

Tertiary sector = branches G-I + J + K + L + M-N + O-Q + R-U.

For the distribution of employment by type of region, the Commission's urban-rural

typology, which classifies NUTS 3 regions into predominantly rural, intermediate and

predominantly urban, will be used.

17

INDICATOR C.13

Indicator Name Employment by economic activity

Type of indicator Sectorial

Definition The indicator gives total employment in agriculture, forestry, the food industry and in tourism in absolute terms and also as a share of total employment.

The absolute change and the annual growth of employment at national level are also calculated in general as a three-year average. If the available data allow, the calculation of five-year averages is also possible.

The Labour Force Survey (LFS) is the main data source for the domain employment. The EU LFS is a large household sample survey providing quarterly results on labour participation of people aged 15 years and over (16 years and over in Spain, Italy and the United Kingdom, 15-74 years in Estonia, Latvia, Hungary, Finland, Sweden and Denmark).

In the Eurostat LFS database (according to the NACE rev.2 divisions) agriculture corresponds to "crop and animal production, hunting and related activities" (A01), while forestry means "forestry and logging" (A02), the food industry is equal to "manufacture of food products" (C10) and "manufacture of beverages" (C11) and "manufacture of tobacco products" (C12), tourism corresponds to "accommodation" (I55) and "food and beverage service activities" (I56).

Unit of measurement 1000 persons and % of total employment

Data source Eurostat – Labour Force Survey

References/location of the data

Eurostat website, Labour Force Survey: lfsa_egan2 and lfsa_egan22d

Regional data: lfst_r_lfe2en2 for totals; employment by economic activity on special request to Eurostat

Data collection level EU, national (NUTS 0) and regional (NUTS 1 and 2)

Frequency Regional data at NUTS 2 level are published as annual averages of quarterly data.

The calculation of the indicator is made on a yearly basis.

Delay 1 year

Comments/caveats

18

INDICATOR C.14

Indicator Name Labour productivity in agriculture

Type of indicator Sectorial

Definition Labour productivity in agriculture is expressed as total Gross Value Added (GVA) in agriculture at basic prices per Annual Work Unit (AWU).

GVA is defined as the value of output less the value of intermediate consumption.

Output is valued at basic prices, GVA is valued at basic prices and intermediate consumption is valued at purchasers’ prices. The basic price is defined as the price received by the producer, after deduction of all taxes on products but including all subsidies on products.

GVA per AWU provides comparable data on labour productivity and allows for comparison between sub-sectors and countries.

A three-year average mitigates short-term fluctuations. Labour productivity in agriculture is then calculated as the ratio of the averages: (three year average GVA) / (three year average labour force).

Unit of measurement EUR/AWU

Data source Eurostat - Economic Accounts for Agriculture

Eurostat - Agricultural Labour Input Statistics

References/location of the data

Economic Accounts for Agriculture for GVA values: aact_eaa01

Agricultural Labour Input Statistics for AWU values: aact_ali01

Data collection level EU and national (NUTS 0)

Frequency Annual

Delay 1 year

Comments/caveats Due to the lack of available regional data, this indicator can only be calculated at national level.

19

INDICATOR C.15

Indicator Name Labour productivity in forestry

Type of indicator Sectorial

Definition Labour productivity in forestry is expressed as total Gross Value Added in forestry at basic prices per Annual Work Unit (AWU).

The forestry sector corresponds to division 02 in NACE rev. 1.1 (Forestry and logging).

GVA is defined as the value of output less the value of intermediate consumption.

Output is valued at basic prices, GVA is valued at basic prices and intermediate consumption is valued at purchasers’ prices. The basic price is defined as the price received by the producer, after deduction of all taxes on products but including all subsidies on products.

GVA per AWU provides comparable data on labour productivity and allows for comparison between sub-sectors and countries.

A three-year average mitigates short-term fluctuations. Labour productivity in forestry is then calculated as the ratio of the averages: (three-year average GVA)/(three-year average employment).

Unit of measurement EUR/AWU

Data source Eurostat - Economic accounts for forestry and logging

Eurostat - Annual work units in forestry and logging

References/location of the data

Eurostat - Economic accounts for forestry and logging [for_eco_cp]

Eurostat - Annual work units in forestry and logging [for_awu]

Data collection level EU and national (NUTS 0)

Frequency Annual

Delay 2-3 years

Comments/caveats Due to the lack of available regional data this indicator can only be calculated at national level and not for all Member States.

20

INDICATOR C.16

Indicator Name Labour productivity in the food industry

Type of indicator Sectorial

Definition Labour productivity in the food industry is expressed as total Gross Value Added (GVA) in the food industry per person employed.

GVA is defined as the value of output less the value of intermediate consumption.

Output is valued at basic prices, GVA is valued at basic prices and intermediate consumption is valued at purchasers’ prices.

The food industry sector corresponds to NACE rev.2 = Manufacture of food products; beverages and tobacco products (C10+C12).

A three-year average mitigates short-term fluctuations. Labour

productivity in the food industry is then calculated as the ratio of the

averages: (three-year average GVA)/(three-year average number of

persons employed).

Unit of measurement EUR/person

Data source Eurostat - National Accounts

Eurostat - Labour Force Survey

References/location of the data

National Accounts: [nama_10_a64]

Labour Force Survey: [lfsa_egan22d]

Data collection level EU and national (NUTS 0)

Frequency Annual

Delay 2 years

Comments/caveats Due to the lack of available regional data, this indicator can only be calculated at national level.

21

INDICATOR C.17

Indicator Name Agricultural holdings (farms)

Type of indicator Sectorial

Definition This indicator consists of five sub-indicators:

Number of agricultural holdings

Agricultural size of the holdings - in utilised agricultural area (UAA) size classes

Economic size of the holdings - in standard output (SO) classes

Labour force - in persons and in annual work units (AWU)

Average size of the holdings - physical (UAA), economic (standard output), labour in persons and AWU

The first four sub-indicators provide basic information on the total number of farms, ha of UAA, EUR of SO and the total number of persons employed or AWU for each EU Member State. Quantities are presented in absolute figures and serve as a basis for the calculation of the fifth sub-indicator.

The physical farm size measured in ha of UAA per farm contains the following size classes:

Zero ha

Less than 2 ha

From 2 to 4.9 ha

From 10 to 19.9 ha

From 20 to 29.9 ha

From 30 to 49.9 ha

From 50 to 99.9 ha

100 ha and over

The economic farm size is measured according to the following classes:

Zero EUR

Less than 2 000 EUR

From 2 000 to 3 999 EUR

From 4000 to 7 999 EUR

From 8000 to 14 999 EUR

From 15 000 to 24 999 EUR

From 25 000 to 49 999 EUR

From 50 000 to 99 999 EUR

From 100 000 to 249 999 EUR

From 250 000 to 499 999 EUR

22

500 000 EUR and over

The fifth sub-indicator provides information on:

the average physical (ha UAA/holding),

economic (EUR of SO/holding)

labour size of the holdings (persons/holding; AWU/holding).

Unit of measurement Farms: number of farms

UAA: number of ha

Labour force: number of AWU, number of persons

Average physical farm size: ha/farm

Average economic farm size: EUR/farm

Average labour force size: person/holding; AWU/holding

Data source Eurostat – Farm Structure Survey (FSS)

References/location of the data

For number of farms, for the physical size (ha of UAA) and for the economic size of farms (SO): [ef_kvaareg]

For the labour force size: [ef_olfreg]

For regional data: [ef_kvecsleg] and [ef_olfreg]

Data collection level EU, national (NUTS 0) and regional (NUTS 1 and 2)

Frequency FSS: full census every 10 years, intermediate surveys 2/3 times in-

between.

Delay 2-3 years

Comments/caveats Persons include regular labour force only.

23

INDICATOR C.18

Indicator Name Agricultural area

Type of indicator Sectorial

Definition The indicator is expressed as the total utilised agricultural area (UAA) in absolute terms (ha) and as the share of UAA in different categories of land use. According to the definition applied in the Eurostat database, these categories of land use are as follows:

Arable land

Permanent grassland and meadow

Permanent crops

Kitchen gardens

A small part of UAA dedicated to kitchen gardens is not considered in this indicator; therefore the share of arable crops, permanent grassland & meadow and permanent crops may not sum up to 100%.

Unit of measurement Number of ha;

% of total UAA

Data source Eurostat – Farm Structure Survey (FSS)

References/location of the data

National and regional data:[ef_oluft]; [ef_oluaareg]

Data collection level EU, national (NUTS 0) and regional (NUTS 1 and 2)

Frequency FSS: full census every 10 years, intermediate surveys 2/3 times in-

between.

Delay 2-3 years

Comments/caveats Codes for land use: arable land (B_1_HA), permanent grassland and meadow (B_3_HA) and permanent crops (B_4_HA).

24

INDICATOR C.19

Indicator Name Agricultural area under organic farming

Type of indicator Sectorial

Definition The indicator shows the number of hectares under organic farming and the share of area under organic farming in the total utilised agricultural area (UAA).

The area under organic farming is classified as follows:

fully converted to organic farming

under conversion to organic farming

total fully converted and under conversion to organic farming

Farming is considered to be organic if it complies with the relevant EU legislation.

The area defined refers to the Utilised agricultural area excluding kitchen garden as from the "Organic crop area by agricultural production methods and crops".

It might not be strictly comparable with the definition of UAA (only area of main crops) in the Farm Structure Survey (FSS).

Data on the area under organic farming at regional level come from the FSS.

Unit of measurement Ha and %

Data source Eurostat – Organic farming

Eurostat – Farm Structure Survey (FSS)

References/location of the data

National data: [ org_cropar_h1] until 2011, [org_cropar] from 2012 onwards

Regional data: [ef_mporganic]

Data collection level EU, national (NUTS 0) and regional (NUTS 1 and 2)

Frequency Organic farming statistics: annual

For FSS data: full census every 10 years, intermediate surveys 2/3 times

in-between.

Delay FSS: 2-3 years

Comments/caveats The % of UAA under organic farming of the EU-28 calculated with data

from the Farm Structure Survey (ef_mporganic) is lower than the same

share calculated with data from the annual statistics on Organic Farming

(food_in_porg1) This difference may be explained by the different

definitions of the UAA and other different requirements (e.g. thresholds)

used in the context of the two surveys.

25

INDICATOR C.20

Indicator Name Irrigated land

Type of indicator Sectorial

Definition The indicator shows the total irrigated land and is expressed in hectares (ha) and as percentage of the total Utilised Agricultural Area (UAA).

Irrigated area is defined as the area of crops which have actually been irrigated at least once during the 12 months prior to the reference day of the survey. Crops under glass and kitchen gardens, which are almost always irrigated, should not be included.

Utilised Agricultural Area consists in the total area taken up by arable land, permanent grassland, permanent crops and kitchen gardens.

As a general assumption, crops under glass (greenhouses) as well as kitchen gardens are considered actually irrigated areas but should not be included here. However, national methodologies may differ when including or excluding 'areas under glass' and 'kitchen gardens' in the 'total irrigated areas'; possible inconsistencies are being scrutinized by Eurostat.

Unit of measurement Ha and %

Data source Eurostat - Farm Structure Survey (FSS), Survey on Agriculture Production Methods (SAPM) 2010.

References/location of the data

National and regional data: table ef_poirrig

Data collection level National (NUTS 0) and regional (NUTS 1 and 2)

Frequency The Survey on Agricultural Production Methods (SAPM) is a once-only

survey carried out in 2010.

Delay 2-3 years (Eurostat, Survey on Agricultural Production Methods)

Comments/caveats The availability of this data source in the future is under discussion.

26

INDICATOR C.21

Indicator Name Livestock units

Type of indicator Sectorial

Definition This indicator gives the total number of livestock units (LU) of the holdings with livestock. LU is a reference unit which facilitates the aggregation of livestock from various species and ages. LU coefficients are used instead of the actual number of animals in order to make comparable aggregations of different animal categories.

Unit of measurement Number of LU

Data source Eurostat – Farm Structure Survey (FSS)

References/location of the data

For national and regional data: [ef_olslsuft] and [ef_olsaareg]; [ef_olslsureg]

Data collection level EU, national (NUTS 0) and regional (NUTS 1 and 2)

Frequency FSS: full census every 10 years, intermediate surveys 2/3 times in-

between.

Delay 2-3 years

Comments/caveats For the coefficients used to calculate the LU in the FSS 2010, see Annex I of Commission Regulation (EC) No 1200/2009: http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=CELEX:32009R1200:EN:NOT

27

INDICATOR C.22

Indicator Name Farm labour force

Type of indicator Sectorial

Definition The indicator shows the labour force directly employed by the agricultural holding and working regularly, in persons and in Annual Work Units (AWU).

The farm labour force of the holding includes all persons having completed their compulsory education (having reached school-leaving age) who carried out farm work on the holding during the 12 months ending on the reference day of the survey. All persons of retirement age who continue to work on the holding are included in the farm labour force.

Total and by sex for the different categories of farm labour force:

regular labour force:

family labour force (sole holders working in the farm + members of the sole holder’s family working in the farm)

non-family labour force

non-regular labour force (only AWU)

Unit of measurement 1000 persons or AWU

Data source Eurostat – Farm Structure Survey (FSS)

References/location of the data

For national data: [ef_olfaa]

For regional data and labour force by sex: [ef_olfreg]

Data collection level EU, national (NUTS 0) and regional (NUTS 1 and 2)

Frequency FSS: full census every 10 years, intermediate surveys 2/3 times in-between.

Delay 2-3 years

Comments/caveats Due to the high share of part-time work in agriculture, labour input can be better assessed in terms of AWU than in terms of persons.

28

INDICATOR C.23

Indicator Name Age structure of farm managers

Type of indicator Sectorial

Definition The indicator shows the total number of managers in different age categories and also the percentage of managers who are younger than 35 years. The indicator also provides information on the ratio between young farm managers (less than 35 years) and farmers of 55 years or older.

The manager of the holding is the natural person responsible for the normal daily financial and production routines of running the holding concerned. The holder is the natural person, group of natural persons or legal person on whose account and in whose name the holding is operated and who is legally and economically responsible for the holding, i.e. who takes the economic risks of the holding. The manager and the holder can be the same person.

Unit of measurement Number of managers, % of total farm managers, ratio of young managers to elderly

Data source Eurostat – Farm Structure Survey (FSS)

References/location of the data

National data: [ef_kvage]

Regional data on special request to Eurostat

Data collection level EU, national (NUTS 0) and regional (NUTS 1 and 2)

Frequency FSS: full census every 10 years, intermediate surveys 2/3 times in-between.

Delay 2-3 years

Comments/caveats Data at regional level (NUTS 1 or 2) on the age of farm managers are not available in the Eurostat public database and need to be requested from Eurostat.

29

INDICATOR C.24

Indicator Name Agricultural training of farm managers

Type of indicator Sectorial

Definition The indicator provides information on the share of farm managers who have attained basic and full education levels in agriculture.

The indicator also shows the share of young farm managers (below 35 years) in total with the different levels (basic and full) of agricultural training acquired by them.

The different categories of agricultural training are defined as follows:

Only practical agricultural experience: experience acquired through practical work on an agricultural holding.

Basic agricultural training: any training courses completed at a general agricultural college and/or an institution specialising in certain subjects (including horticulture, viticulture, sylviculture, pisciculture, veterinary science, agricultural technology and associated subjects). A completed agricultural apprenticeship is regarded as basic training.

Full agricultural training: any training course continuing for the equivalent of at least two years full time training after the end of compulsory education and completed at an agricultural college, university or other institute of higher education in agriculture, horticulture, viticulture, sylviculture, pisciculture, veterinary science, agricultural technology or an associated subject.

In the case of Italy, the definition of "training in agriculture" does not

correspond to the content described above. It refers rather to the

general education level of the farmer. According to the Italian definition:

- practical experience means: the farmer has completed no type of education (primary school, secondary education, higher education);

- basic training means: the farmer completed at least primary education, but did not complete agricultural higher education;

- full training means: the farmer has completed higher or tertiary education at an agricultural college/university/college-level institute/vocational school..

Unit of measurement Number of managers at each level of agricultural training and as percentage of total farm managers

Data source Eurostat – Farm Structure Survey

References/location of the data

National data: [ef_mptrainman]

Regional data: on special request to Eurostat

Data collection level EU, national (NUTS 0) and regional (NUTS 1 and 2)

Frequency FSS: full census every 10 years, intermediate surveys 2-3 times in-between.

Delay 2-3 years

30

Comments/caveats Data at regional level (NUTS 1 or 2) on the training of farm managers are not available in the Eurostat public database and need to be requested from Eurostat.

31

INDICATOR C.25

Indicator Name Agricultural factor income

Type of indicator Sectorial

Definition Agricultural factor income measures the remuneration of all factors of

production (land, capital, labour) regardless of whether they are owned

or borrowed/rented and represents all the value generated by a unit

engaged in an agricultural production activity.

It corresponds to the net value added at factor cost.

Value of agricultural production

- variable input costs (fertilisers, pesticides, feed, etc.)

- depreciation

- total taxes (on products and production)

+ total subsidies (on products and production)

= agricultural factor income (net value added at factor costs)

The indicator consists of two sub indicators:

A. Agricultural factor income per annual work unit (AWU). An AWU in agriculture corresponds to the work performed by one person who is occupied on an agricultural holding on a full-time basis. For this indicator, total (paid and unpaid) AWU are used.

B. The index of agricultural factor income per AWU is already available in Eurostat's Economic Accounts for Agriculture as Indicator A. This index is particularly suited for showing developments over time.

Unit of measurement A. EUR (in real terms)/AWU

B. Index 2005 =100

Data source A. Eurostat, Economic Accounts for Agriculture and Agricultural Labour Input Statistics

B. Eurostat, Economic Accounts for Agriculture

References/location of the data

Agricultural factor income:

Economic accounts for agriculture - values at real prices (aact_eaa04)

Production value at basic price

Factor income: code 26000

Agricultural labour input:

Agricultural Labour Input Statistics: absolute figures (1 000 annual work

units) (aact_ali01)

Index of agricultural factor income/AWU (Indicator A):

Economic accounts for agriculture - agricultural income (indicators A, B, C) (aact_eaa06)

Data collection level A. EU, national (NUTS 0) and regional (NUTS 1 and 2) – where data are

32

available

B. EU and national (NUTS 0)

Frequency Annual

Delay 1 year

Comments/caveats Agricultural factor income is best suited for evaluating the impact of

changes in the level of public support (i.e. direct payments) on the

capacity of farmers to reimburse capital, pay for wages and rented land

as well as to reward their own production factors. In this context one

should note that the proportion of own and external production factors

varies in some cases significantly between and within Member States

and that the remuneration of own and external production factors is

often unequal at farm level.

Regional data are not available for all Member States.

33

INDICATOR C.26

Indicator Name Agricultural entrepreneurial income

Type of indicator Sectorial

Definition Agricultural entrepreneurial income5 measures the income derived from

agricultural activities that can be used for the remuneration of own

production factors, i.e. non-salaried (= family) labour, land belonging to

the agricultural holding and own capital. It is obtained by deducting

wages, rent and interest payments from agricultural factor income (see

C. 25).

Value of agricultural production

- variable input costs (fertilisers, pesticides, feed, etc.)

- depreciation

- total taxes (on products and production)

+ total subsidies (on products and production)

= agricultural factor income (net value added at factor costs)

- wages

- rents

- interest paid

= agricultural entrepreneurial income

In the case of family farms (sole proprietorships), entrepreneurial

income represents, on the one hand, the compensation of the work

performed by the agricultural holder (and the work of non-salaried

family members) and, on the other hand, the income remaining with the

enterprise, without it being possible to separate these two components.

It is, therefore, a mixed income.

A. Agricultural entrepreneurial income per unpaid annual work unit

(AWU) is expressed in absolute terms or as an index.

The index of agricultural entrepreneurial income per unpaid AWU can be

obtained directly from Eurostat's Economic Accounts for Agriculture as

Indicator B.

B. Furthermore, agricultural entrepreneurial income per unpaid AWU is

compared with the average wages in the economy.

The components of the indicator are:

- Agricultural entrepreneurial income (in real and current prices)

- AWU in agriculture, which corresponds to the work performed by one person who is occupied on an agricultural holding on a full-time basis. A distinction is made between salaried and non-

5 See also Annex I Chapter V Agricultural Income Indicators of Regulation (EC) No 138/2004 of the European

Parliament and of the Council of 5 December 2003 on the economic accounts for agriculture in the Community.

34

salaried AWU, which together make total AWU. Agricultural entrepreneurial income is divided by non-salaried AWU in order to show the level of agricultural entrepreneurial income for the farm holder and members of his/her family. In order to compare this "family farm income" with the average wages in the economy, AWUs in agriculture need to be converted into number of hours worked: a standard conversion factor of 1800 hours per AWU and per year is used.

- Gross wages and salaries in all NACE activities at current prices in cash and in kind. Wages and salaries in cash include the values of any social contributions, income taxes, etc. payable by the employee, even if withheld and actually paid directly by the employer on behalf of the employee.

- The total number of hours worked per employee in all NACE activities.

Unit of measurement A: EUR/ non-salaried AWU or index value

B: %

Data source Eurostat – Economic Accounts for Agriculture

Eurostat – Agricultural Labour Input Statistics

Eurostat – National Accounts

References/location of the data

Agricultural entrepreneurial income:

Economic accounts for agriculture - values at current prices (aact_eaa01)

Economic accounts for agriculture - values at real prices (aact_eaa04)

Production value at basic price

Entrepreneurial income: code 31000

Agricultural labour input:

Agricultural Labour Input Statistics: absolute figures (1 000 annual work

units) (aact_ali01)

Index of agricultural entrepreneurial income/non-salaried AWU

(Indicator B):

Economic accounts for agriculture – agricultural income (indicators A, B,

C) (aact_eaa06)

Wages and salaries:

National Accounts by 10 branches - aggregates at current prices: gross

wages and salaries (nama_nace10_c)

Employment:

National Accounts by 10 branches - employment data (nama_nace10_e)

Data collection level A. EU, national (NUTS 0) and regional (NUTS 1 and 2) – where data are available

B. EU and national (NUTS 0)

Frequency Annual

35

Delay 1 year

Comments/caveats It has to be borne in mind that these income aggregates are not

indicators of total income or of the disposable income of households

employed in agriculture, because the latter, in addition to their purely

agricultural incomes, may also have income from other sources (non-

agricultural activities, remuneration, social benefits, and income from

property). In other words, agricultural entrepreneurial income must not

be regarded as farmers' income. Moreover, this measure of income

relates to the income generated by agricultural activities (as well as

inseparable non-agricultural secondary activities) over a given

accounting period, even though in certain cases the corresponding

revenues will not be received until a later date. It does not, therefore,

constitute the income effectively received in the course of the

accounting period itself.

The indicator farm household income cannot be calculated as there is no

methodology or data in Eurostat for this purpose.

Regional data are not available for all Member States. The comparison of agricultural entrepreneurial income with average wages in the economy cannot be done at regional level.

36

INDICATOR C.27

Indicator Name Total factor productivity in agriculture

Type of indicator Sectorial

Definition Total factor productivity (TFP) compares total outputs relative to the total inputs used in production of the output. As both output and inputs are expressed in term of volume indices, the indicator measures TFP growth. The change in production and input volumes is measured over a defined period (2005=100). To aggregate the different output (and input) volume indices, the production (and input) values are used as weights. This allows capturing the relative importance between outputs, or inputs.

TFP reflects output per unit of some combined set of inputs: an increase in TFP reflects a gain in output quantity which is not originating in from an increase of input use.

As a result, TFP reveals the joint effects of many factors including new technologies, efficiency gains, economies of scale, managerial skill, and changes in the organization of production.

TFP index is defined as the ratio between an Output Index (i.e. the change in production volumes over a considered period) and an Input Index (the corresponding change in inputs/factors used to produce them).

Output and input indices are calculated as weighted averages of changes in produced quantities and in input quantities respectively, where the weights are represented by the production value of the various products and the expenditure for each of the four considered production factors (intermediate inputs, land, labour, capital).

Depending on the type of average applied and the chosen reference period for the weights, the TFP indicator assumes different analytical forms. Laspeyres indices are defined as arithmetic means with weighting factors referring to the time 0 (base year), while Paasche indices are harmonic means with weighting factors referring to the time t (current year).

In formula, the TFP Laspeyres index is given by:

LI

LOLTFP

t

tt

_

__

0

00

020100

0

20

20

210

10

1

020100

0

20

20

210

10

1

.../*...**

.../*...**

rr

r

rttt

nn

n

nttt

xxxxi

ix

i

ix

i

i

wwwwq

qw

q

qw

q

q

,

37

while TFP Paasche index is defined as:

PI

POPTFP

t

tt

_

__

0

00

1

21

0

2

2

20

1

1

10

1

21

0

2

2

20

1

1

10

.../*...**

.../*...**

rtttrt

rt

r

t

t

t

t

ntttnt

nt

n

t

t

t

t

xxxxi

ix

i

ix

i

i

wwwwq

qw

q

qw

q

q

,

where jtq and kti

are respectively the quantity of product j and factor k

at time t, while jtw and ktx

are the weights of product j and factor k within the agricultural sector.

Finally, the geometrical average of the Laspeyres and the Paasche index gives the Fischer index, which benefits from the most suitable statistical properties. In formula, the TFP Fisher index is computed as follows:

PTFPLTFPFTFP _*__

Unit of measurement Index, 3 year-average

Data source The Economic Accounts for Agriculture (EAA) from Eurostat.

The volume indices calculated by Eurostat are Laspeyres indices and

changes in volume are measured using the weightings for the preceding

year to guarantee the weightings are relatively up-to-date (see Reg. N°

138/2004). They correspond to the term qlt/ql0 of the equations

displayed above.

Complementary data is required from:

- the Farm Structure Survey (FSS - Eurostat) to assess the share of rented

land (in order to correct the weight of land by including the own land).

- the Agricultural Production Data – Crop Products (Eurostat) for the

volume index of the UAA.

- the Farm Accountancy Data Network (FADN) to estimate the national average depreciation rate.

References/location of the data

EAA:

- Change in output volume (qlt/ql0): Volume Indices, n-1 = 100, Production

value at producer price (aact_eaa05)

- Output weights: Real price in Euro, 2010 = 100, Production value at

producer price (aact_eaa04)

- Change in input volume (ilt/il0) for every input except land and labour

cost: Volume Indices, n-1 = 100, Production value at basic price

(aact_eaa05)

- Input weights: Real price in Euro, 2010 = 100, Production value at basic

price (aact_eaa04)

- Volume index for labour costs: Change in Total labour input measured

38

in 1000 AWU (aact_ali01)

- Correction of the weight for labour costs to cover the family labour

costs: the compensation of employees is divided by the share of paid

labour also directly available from the EAA (aact_ali01)

- Volume index for land costs: Change in Total UAA available in the EAA

(apro_cpp_luse).

FSS: (ef_mptenure)

APD: (apro_cpp_luse)

Data collection level National (NUTS 0)

Frequency Annual

Delay Year N-2

Comments/caveats The calculation of regional values is not possible due to the lack of data at such detailed geographical level.

39

INDICATOR C.28

Indicator Name Gross fixed capital formation in agriculture

Type of indicator Sectorial

Definition

The indicator measures the investments in assets which are used repeatedly or continuously over a number of years to produce goods in agriculture. It is measured in absolute terms and as a percentage of Gross Value Added (GVA) in agriculture.

Gross fixed capital formation (GFCF), which measures how much of the value added is

invested rather than consumed, is a key element for future competitiveness

Unit of

measurement

EUR million (in current prices);

% of GVA in agriculture.

Data source Eurostat – Economic Accounts for Agriculture

Eurostat – National Accounts (for GVA)

References

/location of the

data

Eurostat– Economic Accounts for Agriculture

National data: table aact_eaa01

Regional data: table [agr_r_accts]

Data also available from Eurostat – National Accounts (for GVA)

National data: table [nama_10_a64] (GVA in agriculture, forestry and fishing)

Regional data: table [nama_10r_3gva] (GVA in agriculture, forestry and

fishing) and [nama_10_2gfcf]

Data collection level National (NUTS 0)

Regional (NUTS 1 and 2).

Frequency Annual

Delay The expected delay will be two to three years, depending on the database.

Comments

/caveats

The value of GVA in the primary sector was used when there was no value of GVA in

agriculture.

40

INDICATOR C.29

Indicator Name Forest and other wooded land (FOWL)

Type of indicator Sectorial

Definition

The indicator is defined as the total area of forest and other wooded land (FOWL).

The share of FOWL in the total land area is also shown.

The indicator provides a frame of reference for European forest resources.

Forest is defined as "Land spanning more than 0.5 ha with trees higher than 5 meters and a canopy cover of more than 10 percent, or trees able to reach these thresholds in situ. It does not include land that is predominantly under agricultural or urban land use".

Moreover:

Forest is determined both by the presence of trees and the absence of other predominant land uses. The trees should be able to reach a minimum height of 5 meters in situ.

Forest includes:

areas with young trees that have not yet reached but which are expected to reach a canopy cover of 10 percent and tree height of 5 meters. It also includes areas that are temporarily unstocked due to clearcutting as part of a forest management practice or natural disasters, and which are expected to be regenerated within 5 years. Local conditions may, in exceptional cases, justify that a longer time frame is used.

forest roads, firebreaks and other small open areas; forest in national parks, nature reserves and other protected areas such as those of specific environmental, scientific, historical, cultural or spiritual interest.

windbreaks, shelterbelts and corridors of trees with an area of more than 0.5 ha and width of more than 20 meters.

abandoned shifting cultivation land with a regeneration of trees that have, or is expected to reach, a canopy cover of 10 percent and tree height of 5 meters.

areas with mangroves in tidal zones, regardless whether this area is classified as land area or not.

rubber-wood, cork oak, energy wood and Christmas tree plantations.

with bamboo and palms provided that land use, height and canopy cover criteria are met.

Forest excludes tree stands in agricultural production systems, such as fruit tree plantations (incl. olive orchards) and agroforestry systems when crops are grown under tree cover. Note: Some agroforestry systems where crops are grown only during the first years of the forest rotation should be classified as forest (Source: Forest Resources Assessment, 2010, modified).

Other wooded land (OWL) is defined as "Land not classified as “Forest”, spanning more than 0.5 ha; with trees higher than 5 meters and a canopy cover of 5-10 percent, or trees able to reach these thresholds in situ; or with a combined cover of

41

shrubs, bushes and trees above 10 percent. It does not include land that is predominantly under agricultural or urban land use.

Moreover, the definition above has two options: a) The canopy cover of trees is between 5 and 10 percent; trees should be higher than 5 meters or able to reach 5 meters in situ, or b) The canopy cover of trees is less than 5 percent but the combined cover of shrubs, bushes and trees is more than 10 percent. Includes areas of shrubs and bushes where no trees are present.

OWL includes:

areas with trees that will not reach a height of 5 meters in situ and with a canopy cover of 10 percent or more, e.g. some alpine tree vegetation types, arid zone mangroves, etc.

areas with bamboo and palms provided that land use, height and canopy cover criteria are met (Source: Forest Resources Assessment, 2010).

For terms and definitions see also the following document: http://www.unece.org/fileadmin/DAM/timber/soef/Definitions_Quantitative_indicators_for_website.pdf

Unit of

measurement

1000 ha

% of total land

Data source

Primary source: FOREST EUROPE, UNECE and FAO enquiry on pan-European

quantitative indicators: State of Europe's Forests Report,

http://www.unece.org/forests/fr/outputs/soef2011.html.

Eurostat, Forestry statistics

References/location

of the data

Data availability at present: State of Europe's Forests Report (SoEF), 2011, Indicator

1.1, Forest area, Table A1.2: Extent of forest and other wooded land, 2010

(http://www.unece.org/forests/fr/outputs/soef2011.html).

Data are also available in Eurostat database, Forestry statistics:

FOWL: Table: for_area

Total area: table demo_r_d3area

Data collection level National (NUTS 0).

Frequency Every 5 years (e.g. 2010, 2015).

Delay 1 year (e.g. data of 2015 available in 2016).

Comments

/caveats

For Croatia, Eurostat reports no figure for total area; land area was used to fill the

gap.

42

INDICATOR C.30

Indicator Name Tourism infrastructure

Type of indicator Sectorial

Definition Tourism infrastructure in rural areas is measured as the number of bed places in tourist accommodations in absolute values and as a share of total bed places by degree of urbanisation.

From 2012, data are collected at NUTS 2 level, according to Regulation (EU) No 692/2011 concerning European statistics on tourism and repealing Council Directive 95/57/EC.

Under this regulation, data are collected according to the following degrees of urbanisation:

1. Densely-populated area (cities/large urban area)

2. Intermediate urbanized area (towns and suburbs/small urban area)

3. Thinly populated area (rural area)

According to the definitions of urban areas used in Europe (in line with the United Nations Population Division (UNPD)), urban areas correspond to densely populated and intermediate density areas while rural areas equal thinly populated areas.

Unit of measurement Total number of bed places by degree of urbanisation and % of national total

Data source Eurostat – Tourism statistics

Data according to Regulation (EU) 692/2011 of the European Parliament and of the Council are available from 2012 onwards.

Regional data are available from 1990 onwards.

References/location of the data

For national data: [tour_cap_natd]

For regional data: [tour_cap_nuts2d]

Data collection level EU, national (NUTS 0) and regional (NUTS 1 and 2)

Frequency Annual and monthly.

Annual for regional data.

Delay 1 year

Comments/caveats Collective tourist accommodation establishments include hotels, holiday and other short-stay accommodation, camping grounds, recreational vehicle parks and trailer parks (NACE r.2 divisions I551-I553).

When the number of bed places in one category of establishment is missing, the sum of available data is provided.

The number of bed places in an establishment or dwelling is determined by the number of persons who can stay overnight in the beds set up in the establishment (dwelling), ignoring any extra beds that may be set up by customer request. The term bed place applies to a single bed, double bed being counted as two bed places. The unit serves to measure the capacity of any type of accommodation. A bed place is also a place on a

43

pitch or in a boat on a mooring to accommodate one person. One camping pitch should equal four bed places if the actual number of bed places is not known.

44

INDICATOR C.31

Indicator Name Land Cover

Type of indicator Environment

Definition

The indicator measures the area in the different categories of land cover:

- total agricultural area (agricultural area and natural grassland);

- total forest area (forest area and transitional woodland-shrub);

- natural area;

- artificial area;

- other area (includes sea and inland water).

Land cover is the actual distribution of forests, water, desert, grassland and other physical features of the land, including those created by human activities. Land use, on the other hand, characterises the human use of a land cover type.

The data source used is CORINE Land Cover (CLC).

CLC databases are obtained through computer-assisted interpretation of satellite images acquired in 1990, 2000, 2006 and 2012, offering the possibility to describe the geographic distribution of specific land cover changes in a geo-referenced approach.

CLC describes land cover (and partly land use) with a three-level nomenclature of 44 classes. For the purpose of this indicator, they have been grouped so as to obtain the four classes of agricultural, forest, natural and artificial land cover. CLC was elaborated based on the visual interpretation of satellite images (IRS, RapidEye, Spot, Landsat TM and MSS). Ancillary data (aerial photographs, topographic or vegetation maps, statistics, local knowledge) is used to refine interpretation and assign classes. The CLC database is based on a standard production methodology characterised by the following elements: Mapping scale is 1:100 000. Mapping accuracy is 100 m. The minimum mapping unit for the inventory is 25 ha for areas, and 100 m for linear elements. Level 1 Level 2 Level 3 Reclassification

1 Artificial surfaces 1.1 Urban fabric Artificial

1.2 Industrial, commercial and transport units Artificial

1.3 Mine, dump and construction sites Artificial

1.4 Artificial, non-agricultural vegetated areas Artificial

2 Agricultural areas 2.1 Arable land Agricultural

2.2 Permanent crops Agricultural

2.3 Pastures Agricultural

2.4 Heterogeneous agricultural areas Agricultural

3 Forest and seminatural areas 3.1 Forests Forest

3.2Scrub and/or herbaceous vegetation

associations3.2.1 Natural grasslands Agricultural

3.2.2 Moors and heathland Natural

3.2.3 Sclerophyllous vegetation Natural

3.2.4 Transitional woodland-shrub Forest

3.3 Open spaces with little or no vegetation Natural

4 Wetlands 4.1 Inland wetlands Natural

4.2 Maritime wetlands Sea

5 Water bodies 5.1 Inland waters Inland water

5.2 Marine waters Sea It should be noted that other sources may give significantly different results, but CLC has a uniform methodology and nomenclature across Europe. Moreover, it is the only dataset which is complete for the EU-28.

Nevertheless, in order to reduce and explain the discrepancies with other surveys and national inventories, the estimation of agricultural areas and forest includes separately the CLC classes "Natural grassland" and "Transitional woodland –shrubs", which are, in most cases, likely to be critical in the estimation.

45

Unit of

measurement

% of total area

Data source CORINE Land Cover (CLC) 2012

References/location

of the data

European Environment Agency

http://www.eea.europa.eu/data-and-maps/data/corine-land-cover-2006-raster-3

http://land.copernicus.eu/pan-european/corine-land-cover/clc-2012/view

Data collection level Regional (NUTS 2).

Frequency

CORINE Land Cover: 6 years

Delay CORINE Land Cover: 3 years (e.g. CLC 2012 available in 2015).

Comments/caveats

46

INDICATOR C.32

Indicator Name Areas facing natural and other specific constraints - ANCs6

Type of indicator Environment

Definition

The characterisation of agricultural areas designated as areas facing natural or other specific constraints provides useful information on the environment in which the policy is implemented.

The indicator measures the share of agricultural area in different categories of areas facing natural or other specific constraints (ANCs) (ex-LFAs as they were defined in the period 2007-2013):

Mountain areas (incl. areas north of the 62nd parallel) (ex-LFA mountain);

Areas, other than mountain areas, facing significant natural constraints (ex-LFA intermediate);

Other areas affected by specific constraints (ex-LFA specific).

Article 32 of Regulation (EU) No 1305/2013 defines the areas facing natural or other specific constraints, which are eligible for payments to farmers. They are classified according to three categories, each of which describes a specific cluster of natural or other specific constraints affecting agricultural production in the area concerned.

The area designations and other requirements for ANCs have changed in comparison to the programming period 2007-2013 and to Regulation (EC) No 1698/2005 and Regulation (EC) No 1257/99, which are repealed.

While no revision of the designation of mountain areas or areas affected by specific constraints is foreseen in Regulation (EU) No 1305/2013 (apart from the possibility to apply the combination of biophysical criteria for the designation of areas affected by specific constraints), areas facing natural constraints other than mountain (former LFA intermediate) should be delimited according to eight biophysical criteria, as defined in Annex III of Regulation (EU) No 1305/2013, covering climate, poor soil productivity and steep slopes. Each (sub)criterion has a predefined threshold, e.g. slopes with a gradient of 15% (or more) which identifies the trigger for the area to be considered as severely constrained from the agricultural production point of view. Measurement of constraint(s) takes place at the level of Local Administrative Units (LAU) 2 (which corresponds to municipality level in most Member States) or at the level of clearly delineated, contiguous local units. The latter should have a definable economic and administrative identity. Farming in the respective local unit can be considered as being constrained if constraints are present on at least 60 % of the local unit's agricultural area. The new legislation also stipulates the mechanism of fine-tuning, i.e. for excluding those administrative units where a constraint has been documented but it has been overcome by investments or by economic activity. The fine-tuning exercise is a part of the designation exercise. New data on ANCs are reported by Member States within the programming period 2014-2020.

Unit of % of the utilised agricultural area (UAA)

6 According to the COMMISSION IMPLEMENTING REGULATION (EU) No 808/2014 this indicator is called

"Less-favoured areas"

47

measurement

Data source

DG Agriculture and Rural Development: data are reported by Member States during the programming period 2014-2020.

References/location

of the data

DG Agriculture and Rural Development on request.

Data collection level

National, regional.

Areas facing natural constraints, other than mountain: Data are reported at the level

of LAU2 or another LAU (a number of Member States use different administrative

units for the delimitation of these areas).

Areas affected by specific constraints: Data collection may be carried out at the level

of area designation. In case the designation is carried out according to the

"combination of biophysical criteria", as defined in the third paragraph of Article 32(4)

of Regulation (EU) No 1305/2013, the data collection level should be LAU2 or another

clearly delineated local unit.

Frequency Data available at present: At the moment data on LFAs are from 2005 (and from 2007

in the case of BG and RO).

Delay

Comments/caveats

Member States may complete the delimitation of the ANCs other than mountain

(former LFA intermediate) until the end of 2017. Meanwhile, the previous area

designations are in force.

48

INDICATOR C.33

Indicator Name Farming intensity

Type of indicator Environment

Definition

This indicator consists of 2 sub-indicators:

1. Farm input intensity

2. Areas of extensive grazing

1. Farm input intensity is expressed as the utilised agricultural area (UAA) managed by farms with low, medium and high input intensity, as percentage of total UAA.

The input intensity of a farm can be defined as the level of inputs used by the farm per unit of production factor (in general land). Intensification is defined as the increase in farm intensity, while extensification describes the opposite trend.

Farms are classified into intensity categories according to an estimate of input volume per hectare of UAA. The inputs considered are fertilizers, pesticides, other crop protection products and purchased feed. This approach allows covering both crop and livestock productions.

The volume of inputs used (per hectare) is estimated by dividing input expenditures (per hectare) by the input price index for the year and country in question. This results in input expenditures per hectare in constant national input prices. Fertiliser expenditure (purchased fertilisers and soil improvers) is divided by the fertiliser price index in the country of the same year in order to estimate the volume used. Similarly, crop protection expenditure (plant protection products, traps and baits, bird scares, anti-hail shells, frost protection) is divided by the pesticide price index in the country of the same year. Purchased feed cost is also divided by the feed price index in the country of the same year. The result is thus expressed in constant inputs prices (Euro per ha). The method allows not only to deduct inflation, but also the fluctuation of input prices. Thus it estimates the trend in the volume of inputs used per hectare. However, it does not capture differences in input prices between countries and the differences in prices within each category of inputs (for example between a pesticide A and a pesticide B). Therefore it does not give the exact volume of inputs used for a specific country and year.

In a second step, each farm is classified according to its average level of input use per ha. The thresholds have been set in such a way that the UAA in the EU-25 is equally divided into the three categories for the first year of the analysis (2004 for the EU-25). If it is higher than 300 constant EUR per ha (the threshold separating the highest from the middle category), the farm is qualified as having high intensity. When it is below 130 constant EUR per ha (the threshold separating the middle from the lowest category), it is classified as having low intensity. Otherwise, it is considered to have medium intensity. These levels do not pretend to represent any real borders of extensive and intensive farming. They are just set in a pragmatic way to study the developments in farming intensity.

To properly interpret the results of this indicator, an "average level of intensity" in the country can also be calculated. Intensification in a country with very low intensity

49

does not mean the same for the environment than intensification in a country with high intensity. That is the purpose of the variable, "average input expenditures per hectare in constant input prices". It is not the ideal measurement of intensity; however, it is the best estimate that we can obtain until now from the available data.

For the calculation of this indicator, the methodology of the agri-environmental indicator 12 "intensification/extensification" is used:

http://ec.europa.eu/eurostat/statistics-explained/index.php/Agri-environmental_indicators

2. Areas of extensive grazing measures the area under grazing livestock production (cattle, sheep and goats), where the stocking density does not exceed 1 livestock unit per ha of forage area (forage crops, permanent pastures and meadows), as share of the total UAA.

For each region (NUTS 2), the livestock density per ha of forage UAA (fodder crops + permanent grassland) is calculated. If the livestock density is less than 1 LU/ha the regions is classified as extensive; if it is more than 1 LU/ha, the regions is classified as intensive. Then the share of forage UAA in total UAA for each "extensive" NUTS 2 is calculated.

In each region the following information is needed:

- total UAA, UAA used for fodder crops and UAA for permanent grassland;

- numbers of grazing livestock (bovine by sex and age, sheep, goats);

- livestock numbers are converted into livestock units.

The conversion of the number of animals into livestock units has to be made by using the coefficients listed in Annex I of Commission Regulation (EC) No 1200/2009 (http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=CELEX:32009R1200:EN:NOT).

Forage crops are defined as characteristic D18 (forage plants) of the Farm Structure Survey.

Since the evaluation of the extensive character of agriculture should be made at the most detailed geographical level possible, the extensive character of agriculture at Member State level is determined by aggregating values at NUTS 2 level.

Unit of

measurement

1. % of total UAA. (The variable "average level of intensity" per country or region is expressed in "Euro per ha in constant input prices").

2. ha and % of total UAA

Data source

1. Farming intensity

- The main data source is DG AGRI - Farm Accountancy Data Network (FADN).

- Eurostat - Price indices of the means of agricultural production, input.

2. Eurostat - Farm structure survey (FSS).

References/location

of the data

1. Farming intensity

- DG AGRI - Farm Accountancy Data Network (FADN). Name of current variables

defined in the FADN: SE295 Fertilizers; SE300 Crop protection; F64 to F67 Purchased

feedstuffings, SE025 Utilised Agricultural Area:

50

http://ec.europa.eu/agriculture/rica/;

https://circabc.europa.eu/faces/jsp/extension/wai/navigation/container.jsp

- Eurostat - Price indices of the means of agricultural production, input, annual data:

table apri_pi05_ina. Products: 203000 - FERTILISERS AND SOIL IMPROVERS, 204000 -

PLANT PROTECTION PRODUCTS AND PESTICIDES, 206000 - ANIMAL FEEDINGSTUFFS

(e.g. Index 2005=100, nominal value).

2. Areas of extensive grazing

- Eurostat - Farm structure survey (FSS), Tables: Land use: number of farms and areas of different crops by agricultural size of farm (UAA) and NUTS 2 regions [ef_oluaareg]; Livestock: number of farms and heads of animals of different types by agricultural size of farm (UAA) and NUTS 2 regions [ef_olsaareg].

Data collection level 1. National (NUTS 0), regional (NUTS 2); primary data refer to FADN regions.

2. National (NUTS 0), regional (NUTS 2)

Frequency 1. Annual

2. FSS: full census every 10 years, intermediate survey 2-3 times in-between.

Delay 1. 2 years

2. 2-3 years

Comments/caveats

1. Farming intensity

- For more caveats and details on the methodology refer to the agri-environmental

indicator 12 Intensification/Extensification

http://ec.europa.eu/eurostat/statistics-explained/index.php/Agri-environmental_indicators

51

INDICATOR C.34

Indicator Name Natura 2000 areas

Type of indicator Environment

Definition

This indicator consists of 3 sub-indicators :

• % of territory under Natura 2000

• % of UAA under Natura 2000

• % of forest area under Natura 2000

This indicator provides information on the preservation of the natural environment and landscape and on the protection and improvement of natural resources. Under Natura 2000, a network of areas is designated to conserve natural habitats and species of wildlife which are rare, endangered or vulnerable in the European Union.

The Natura 2000 network consists of sites:

• designated by Member States as Special Protection Areas (SPA) under the Birds Directive (Council Directive 79/409/EEC of 2 April 1979),

• proposed by Member States as Sites of Community Importance (pSCI) and later designated as Special Areas of Conservation (SAC) under the Habitats Directive (Council Directive 92/43/EEC of 21 May 1992).

For the Special Protection Areas designated under the Birds Directive, the responsibility for designation lies entirely with the Member States. The Commission (DG Environment) has to be informed when new areas are designated or existing areas are modified. The information received on new or revised areas is passed on to the European Environment Agency (EEA), which regularly produces consolidated versions of the SPA database for the whole EU.

For the proposed Sites of Community Importance, which will in the future be Special Areas of Conservation under the Habitats Directive, there is a three-stage process that starts with a proposal by Member States. The proposals are transferred to the Commission, which evaluates with the European Topic Centre on Biological Diversity (ETC_BD) whether or not the proposed sites ensure sufficient protection and, on the basis of that evaluation, asks the Member States to propose more sites whenever necessary. The EEA regularly (once a year) compiles all the information received into a single EU database.

The lists of sites foreseen in the Habitats Directive are divided into nine bio-geographic regions (Pannonian, Boreal, Continental, Atlantic, Alpine, Macaronesian, Black Sea, Steppic and Mediterranean) within the territory of the Union. The first list for the Macaronesian region was agreed in December 2001. The second list was adopted in December 2003 for the Alpine region, followed in 2004 by the lists for the Continental and Atlantic regions. The list for the Boreal region was adopted in 2005, and the list for the Mediterranean region in 2006. The lists for the Steppic and the Black Sea regions were adopted in 2008.

Natura 2000 sites include different types of European ecosystems. Some sites are in coastal areas or in open marine waters, some contain lakes or are riverine, and many include forest and farmland. For calculating an improved version of this indicator, geo-referenced information is required.

The data sets used consist of the Natura 2000 Spatial Dataset and the CORINE Land Cover (CLC) raster dataset. Although CLC categories do not fully correspond to the statistical definitions of agricultural area (UAA) or forests, the overlay of the two data sets allows an accurate geographical estimation of land use data inside Natura 2000

52

sites.

To reduce and explain the discrepancies with other surveys and national inventories, the estimation of the UAA and forest includes separately the CLC classes "Natural grassland" and "Transitional woodland –shrubs".

CLC classes are aggregated to estimate:

- Total farmland (UAA): CLC classes 2xx and 321

- Forest area : CLC classes 31x and 324

Please note that the situation regarding Natura 2000 sites is constantly evolving and

therefore the data represent only a snapshot of the situation at a reference date.

The figures relating to the area coverage of Natura 2000 sites have been obtained by

GIS analysis performed by DG Environment and the EEA.

Unit of

measurement

% of area under Natura 2000

Data source

Natura 2000 Barometer provided by DG Environment – ETC_BD

Natura 2000 Spatial Dataset 1: 100.000 Scale

CORINE Land Cover (CLC) (e.g. latest version is CLC 2012 v.18_4).

References/location

of the data

European Commission – DG Environment

Natura 2000 Barometer

http://ec.europa.eu/environment/nature/natura2000/barometer/index_en.htm

Natura 2000 Spatial Dataset

http://www.eea.europa.eu/data-and-maps/data/natura-5

Data collection level Regional (NUTS 2).

Frequency

Natura 2000: every year

CORINE Land Cover: every 6 years

Delay Natura 2000: 1 year

CORINE Land Cover: 3 years (e.g. CLC 2012 is available in 2015).

Comments/caveats

53

INDICATOR C.35

Indicator Name Farmland birds index (FBI)

Type of indicator Environment

Definition (Exact

definition in bold,

description of the

measurement/calcu

lation, sub-

indicators in bold if

relevant).

The farmland bird indicator is intended as a barometer of change for the biodiversity of agricultural landscapes in Europe.

The indicator is a composite index that measures the rate of change in the relative

abundance of common bird species at selected sites: trends of index of population

of farmland birds (base year 2000 = 100).

These species, chosen from a list of selected common species at EU level (the so-

called "EU list of species" currently cover 39 species7), are dependent on farmland for

feeding and nesting and are not able to thrive in other habitats. The species on the

list constitute a maximum, from which the countries select the species relevant to

them. However, Member States can select their own species set, ideally following

guidelines from the European Bird Census Council (EBCC). No rare species are

included in EU species selection. Population trends are derived from the counts of

individual bird species at census sites and modeled as such through time.

The population counts are carried out by a network of volunteer ornithologists

coordinated within national schemes. Indices are first calculated for each species

independently at the national level by producing a national population index per

species. Then, to produce the EU aggregate, the national species indices are

combined into supranational ones. To do this, they are weighted by estimates of

national population sizes. Weighting allows for the fact that different countries hold

different proportions of the European population of each species. In a third step, the

supranational indices for each species are then combined on a geometric scale to

create a multi-species aggregate index at European level. For more detailed

information on the methodology used, species, etc. please refer to the EBCC website

http://www.ebcc.info/ and the Eurostat indicator metadata.

The index is calculated with reference to a base year, when the index value is set at

100%. Trend values express the overall population change over a period of years. (In

Eurostat's database, data are presented with four different bases: 1990, 2000, the

latest year available and the national base year). Data going back to the 1980s

however exist and are available at the EBCC website.

The indicator already exists:

o Agro-environmental indicator (AEI) 25: Population trends of farmland birds;

o Sustainable development indicators (SDI) – Biodiversity: Common Birds Index

(Eurostat).

o SEBI indicator 01: abundance and distribution of selected species, which includes common farmland bird index (Pan-European Streamlining European Biodiversity Indicators (SEBI) initiative, European Environment Agency (EEA), Directorate-General Environment (DG ENV) , etc.)

7 2014 update

54

Unit of

measurement

Index - (base year 2000 = 100)

Data source

EBCC/RSPB/BirdLife/Statistics Netherlands: the European Bird Census Council (EBCC)

and its Pan-European Common Bird Monitoring Scheme (PECBMS),

http://www.ebcc.info/pecbm.html .

Data are transmitted to Eurostat and published in the statistics database:

Environment/Biodiversity. Eurostat does not receive any of these data directly from

the Member States.

National indices are compiled by each country using common software and

methodology. The supranational indices are compiled by the Pan-European Common

Bird Monitoring scheme (PECBM), a joint project of the European Bird Census Council,

the Royal Society for the protection of Birds, BirdLife International, and Statistics

Netherlands.

References/location

of the data

Location of the data:

Eurostat, Environment statistics, Biodiversity: Table env_bio2: Common farmland bird

index.

References

- EBCC/PECBMS : European Birds Census Council/ Pan-European Common Bird

Monitoring Scheme http://www.ebcc.info/pecbm.html;

- AEI 25 "Population trends of farmland birds", as defined in the COM (2006) 508 on

"Development of agri-environmental indicators for monitoring the integration of

environmental concerns into the CAP",

http://epp.eurostat.ec.europa.eu/portal/page/portal/agri_environmental_indicators/

introduction.

Data collection level

National and EU level aggregation (on the basis of the number of Member States

which delivered data every year. E.g.: in 2008 only 15 Member States delivered data;

for the 2014 EBCC/PECBMS updates data is available for 25 EU Member States, from

1990 to 2012).

In the future the index could be calculated at a lower level, by bio-geographical areas

(different agricultural habitats) on the basis of geo-referenced data (France already

does it, but no harmonized data at EU level at the moment exist).

Frequency

Annual (In principle, these data are updated on a yearly basis at national and EU level. Ability to provide updates of indicators at national level depends on the capacity of the national data providers).

Data availability in early 2015: 2008 national data, EU aggregates 2012.

For a small number of Member States data are available from 1980 and cover

different periods depending on data availability in each Member State. However,

Eurostat considers 1990 to be the first year with sufficient geographic coverage for

the EU as a whole and therefore time series should be calculated from 1990.

Delay 2-3 years)

Comments/caveats Comparability between Member States is possible since the index gives a measure of

55

the rate of change in the abundance of common bird species. Species may differ in

each Member State because their relevance changes in different agricultural habitats

and their geographical distribution is not pan-European. Northern countries generally

have fewer species than southern ones.

Coverage has increased from nine to twenty-two EU Member States over the period

1990 to 2010, with three more countries covered as of the reference year 2011. As

for the time series, the number and type of species chosen from the selected

common list by each country should remain stable over time unless solid justification

is provided. If a country decides to include more species, this is often because older

data have become available in electronic form. In any case, the whole time series of

each country is re-calculated for each new data delivery and may therefore change

over time, because the indicators are all calculated with modelling technique.

There have recently been changes to the species covered and the time series for

several countries. The fluctuations between model runs show that small rises or falls

in the indicator should not be regarded as anything real and that it is best to look only

at the change between 1990 and the latest available year.

It should be noted that some countries publish national bird indices based on a

different selection of species than the one used for the EBCC computations, or on a

different allocation of species to habitats. This approach can be used as well as long

as general principles for production of national common bird indicators are applied.

Time series start from 1990 (for the period 1980-1989 data are not representative at

EU level), but may be earlier for the national time series.

56

INDICATOR C.36

Indicator Name Conservation status of agricultural habitats (grassland)

Type of indicator Environment

Definition

The indicator shows the conservation status of agricultural habitats (grassland) and it measures the percentage of assessments of agricultural habitats (grassland) that have a favourable, unfavourable-inadequate and unfavourable-bad conservation status.

It will be used to assess changes in the conservation status of European grassland ecosystems.

The indicator is based on data collected according to monitoring obligations under

Article 11 of the Habitats Directive (92/43/EEC) and is therefore part of the indicator

"Habitat types of European interest" (SEBI indicator 05). It is important to note that

Article 11 monitoring is not limited to the Natura 2000 network, but covers the entire

European territory of the EU Member States.

The indicator covers a set of habitat types (listed in Annex I of the Habitats Directive)

which are considered to be of European interest that are linked to agro- and grassland

ecosystems. This set comprises:

habitats which are in danger of disappearance in their natural range;

habitats that have a small natural range following their regression or by reason

of their intrinsically restricted area;

habitats which present outstanding examples of typical characteristics of one

or more of the biogeographical regions' (Article 1 of the Habitats Directive).

Trends in this indicator should primarily be influenced by the implementation of

measures under the Habitats Directive, such as the establishment of the Natura 2000

Network and habitats and species protection measures. Therefore the indicator reflects

progress achieved by the Habitats Directive, one of the main legislative pillars of EU

nature conservation policy.

EU Member States have to monitor and report the conservation status of habitats of

European interest. Habitat types in agro- and grassland ecosystems, which are

significant are also individually monitored (e.g. certain types of species-rich grasslands,

pastures, moors and heathlands, wetlands). The conservation status is illustrated in

three 'traffic light' categories ('favourable' – green, 'unfavourable inadequate' – amber,

'unfavourable bad' – red, plus unknown) characterised by four parameters:

1. trends and status of range,

2. trends and status of the area,

3. structure and function including typical species,

4. future prospects.

The indicator is based on the number of habitats in the three conservation status

categories and on changes between categories in time.

The percentage of grassland types assessed as being in "Favourable" / "Unfavourable-

57

inadequate" / "Unfavourable-bad" conditions in the total grassland is calculated.

More information on conservation status assessment:

http://biodiversity.eionet.europa.eu/article17

http://biodiversity.eionet.europa.eu/article17/Art%2017_consultation_notes.pdf

The indicator will enable an assessment of the level of ambition of the Natura 2000 measures proposed by Member States in the Rural Development focus area on biodiversity. The information is complementary to the Farmland Bird Index, which is not an indicator of habitats and only focused on common birds. Two "greening" measures of pillar 1 (the Ecological Focus Area (EFA) and the grassland conservation measure) as well as certain cross compliance provisions are complementary key elements which contribute to the improvement of the grassland conservation status.

More information on this indicator:

http://www.eea.europa.eu/data-and-maps/indicators/habitats-of-european-interest

http://www.eea.europa.eu/publications/eu-2010-biodiversity-baseline/: section on

agricultural habitats of the EU2010 Biodiversity Baseline report (p27 and 36).

Unit of

measurement

For each type of assessment:

- % of total assessments of habitats

Data source Data are prepared by DG ENV on request.

References

/location of the

data

Primary source used by DG ENV:

Member State level:

http://www.eea.europa.eu/data-and-maps/data/article-17-database-habitats-

directive-92-43-eec

Conservation Status by Member State and biogeographical region for each species:

http://forum.eionet.europa.eu/x_habitat-

art17report/library/conservation_status/overall_assessment/as_percentage/ms_level/

species

Data collection

level

National level.

In addition, where the territory of a Member State belongs to several biogeographical

regions, and provided a species occurs in more than one of these biogeographical

regions in the Member State, the Member State provides a separate assessment for

that species for each of the biogeographical regions.

Frequency Every 6 years (e.g. latest data cover the period 2000-2006; the next report will cover the period 2007-2012).

Delay 2-3 years (e.g. data available in 2015 refer to the 6-year period 2007-2012)

Comments

/caveats

The indicator is based on data provided by 25 EU Member States (Bulgaria and

Romania will be included in the next reporting phase of 2013-2018) through their

reports under Article 17 of the Habitats Directive.

58

INDICATOR C.37

Indicator Name HNV (high nature value) farming

Type of indicator Environment

Definition

This indicator is defined as the percentage of Utilised Agricultural Area farmed to

generate High Nature Value.

High Nature Value (HNV) farming results from a combination of land use and farming

systems which are related to high levels of biodiversity or the presence of certain

species and habitats.

The common definition established inter alia by the EEA and JRC, recognises three

categories of farmland as HNV:

Type 1: Farmland with a high proportion of semi-natural vegetation

Type 2: Farmland with a mosaic of low intensity agriculture and natural and structural

elements, such as field margins, hedgerows, stone walls, patches of woodland or

scrub, small rivers etc

Type 3: Farmland supporting rare species or a high proportion of European or world

populations.

This indicator is a further development of AEI 23 "High Nature Value Farmland", and

the farmland component of the 2007-2013 CMEF Baseline indicator 18 "High Nature

Value farmland and forestry".

Methodology:

For the purposes of this indicator, the common parameter "HNV farming", as defined

above, is to be assessed within each Member State and individual RDP area using

methods suited to the prevailing bio-physical characteristics and farming systems,

and based on the highest quality and most appropriate data available. The Member

State authorities are responsible for conducting this assessment and providing the

values to the Commission.

Methodological guidance for establishing values for this indicator has been provided

in "The application of the High Nature Value impact indicator" Evaluation Expert

Network (2009) :

http://enrd.ec.europa.eu/app_templates/filedownload.cfm?id=6A6B5D2F-ADF1-

0210-3AC3-AD86DFF73554

Several Member States raised the issue of comparability and/or aggregation if

different methodologies are used. Agreement on the common parameter being

measured, and transparency and acceptance of the various methodologies, whilst not

ideal, allows for aggregation, since in all areas the land considered to fulfil the criteria

for one of the three HNV types is assessed, provided that Member States have

selected methodology appropriate to identifying HNV in their biophysical situation.

The purpose of this indicator is not to make comparisons between territories on the

basis of the extent of HNV land, but rather to consider the trends in its preservation

and /or enhancement. It is therefore important that in each territory the same

methodology is used for each successive assessment, so that trends are estimated

59

correctly.

When more accurate methods are developed, leading to a change in the methodology used, HNV assessments should be recalculated for the baseline year to ensure that the trend can be captured. If this is not possible, then the new methodology should be used alongside the old to allow trends to be assessed.

Unit of

measurement

Percentage (%)

The absolute area of utilised agricultural area (UAA) in hectares, and of HNV

farmland, is also required, to allow for aggregation to Member State/EU level.

Data source

The data sources for estimation of HNV farming are many and varied, and currently

depend on the methods selected by the Member State authorities. Analysis relies

principally on national/regional data, but also includes use of some EU data sets.

Sources include: CORINE and other land cover data, IACS/LPIS, agricultural census

data, species and habitat databases, GIS, specific sampling surveys, RDP monitoring

data, designations (NATURA, national nature reserves etc.).

References/location

of the data

For assessment of HNV farmland national/regional data are required (see above)

UAA: EUROSTAT FSS national and regional data: table ef_oluaareg

Data collection level

The indicator should be established at either national, NUTS 1 or NUTS 2 level. Values

should be obtained which correspond to RDP territory level. Large Member States

may consider it appropriate to have a regional assessment, particularly where there

are large regional variations in climate, topography, biodiversity, landscape and/or

farming patterns.

The level at which the data is available varies with the data source (see description

above).

Frequency

Variable. Minimum requirement is 3 times between 2013 and 2022: a baseline

assessment at the start of the 2014-2020 period (ideally for 2012 or 2013), an

assessment at the end of the period (to coincide with the ex-post evaluation of the

RDP territory), and one update during the period (ideally for 2017 or 2018).

Delay Variable (depends on the data sources used, frequency of surveys/sampling, etc.).

Comments/caveats

Due to the variation in data availability, physical/ecological situation and farming

systems and practices across Member States, it is not appropriate to impose a

common methodology for the assessment of HNV farming. Use of one single method

would restrict the analysis to data available throughout the EU, which would exclude

the richest and most relevant data sources, and preclude those Member States which

have developed more refined methods from using them, with a consequent reduction

in the quality and accuracy of the assessment.

A full assessment of HNV farming would consider both extent and quality/condition.

The indicator definition proposed here only covers the extent of HNV areas, since in

most Member States current methodology is not sufficiently developed to provide

reliable indications of the condition of HNV areas. However, Member States are

strongly encouraged to continue developing and refining the approaches used so that

quality/condition can be incorporated into HNV assessments.

60

Additional information on HNV farming throughout the EU is available in the recently

published book “High Nature Value Farming in Europe”. The Directorate-General

Environment (DG ENV) study on "The High Nature Value farming concept throughout

EU 27 and its maturity for financial support under the CAP" (starting October 2012)

may also provide further information on assessment methodologies which could be a

support to Member States.

As for all other indicators, it is necessary to have an estimated value for this indicator

for all Member States. Until an appropriate specific method for estimating HNV is

identified and used by the Member State authorities, there are two existing sources

of data which could be used in the interim to provide a value, although both have

considerable limitations and do not give a representative assessment of the extent of

HNV. Use of these values is a second-best alternative compared to use of a more

accurate and appropriate method. These data sources are mentioned here solely to

provide an initial fall-back option in cases where a Member State has not yet made

sufficient progress to be able to provide more accurate starting values based on more

appropriate and specific data and methods. The two fall-back options are:

1) Estimation of HNV farmland from CORINE land cover data (EEA study) Limitations:

This approach does not take account of farming systems.

Land cover assessments do not always distinguish well between abandoned land with encroaching scrub, and extensive semi-natural grassland with patches of bushes or scattered trees.

The scale used may mean that smaller areas, such as agricultural parcels within wooded areas are missed completely.

The area of agricultural land estimated from CORINE land cover data does not correspond to EUROSTAT's UAA data.

The EEA exercise is not updated regularly, so it does not provide a dynamic picture.

2) Area of UAA contained within designated NATURA 2000 sites. Limitations:

This approach does not take account of farming systems.

It is static rather than dynamic.

It underestimates the extent of HNV since it primarily addresses only Type 3 HNV farmland rather than all 3 types.

61

INDICATOR C.38

Indicator Name Protected forest

Type of indicator Environment

Definition

Protected forests are important to maintain and enhance biodiversity, as well as to conserve landscapes and provide recreation opportunities (SoEF, 2011).

The indicator measures the share of forest and other wooded land (FOWL) protected to conserve biodiversity, landscapes and specific natural elements according to MCPFE8 Assessment Guidelines (MCPFE classes 1.1, 1.2, 1.3 and 2).

The percentage of protected forest in each MCPFE class is given.

"Protected areas are one of the oldest instruments for protecting nature and natural resources, and are included as a main pillar in nature conservation laws across Europe. Explicitly designated protected areas focus mainly on conserving biological diversity, landscape, natural monuments and protective functions of forests. The MCPFE Assessment Guidelines for Protected and Protective Forest and Other Wooded Land in Europe were created in 2001-2003 especially for European countries where protected forest areas are often small, most of which are located in fragmented landscapes with other land use categories and are protected with various management options and regimes" (SoEF, 2011)

Protected and protective9 forest and other wooded land have to comply with the following general principles according to the MCPFE Assessment Guidelines:

Existence of legal basis

Long-term commitment (minimum 20 years)

Explicit designation for the protection of biodiversity, landscapes and specific natural elements (MCPFE Assessment Guidelines, 2002)

MCPFE Classes for 'Protected FOWL to conserve biodiversity, landscapes and specific natural elements' are defined by the MCPFE Assessment Guidelines for Protected and Protective Forest and Other Wooded Land in Europe (Fourth MCPFE, Vienna 2003). Document available at http://www.foresteurope.org/reporting_SFM.

Class 1: Main Management Objective "Biodiversity":

Class 1.1: “No Active Intervention”:

- The main management objective is biodiversity

- No active, direct human intervention is taking place

- Activities other than limited public access and non-destructive research not detrimental to the management objective are prevented in the protected area

8 The Ministerial Conference on the Protection of Forests in Europe has changed its name from MCPFE to FOREST

EUROPE 9

"Protective forests" under MCPFE class 3, designated to protect soil and its property or water quality and quantity

or other forest ecosystem functions, or to protect infrastructure and managed natural resources against natural

hazards, are not considered in this indicator.

62

Class 1.2: “Minimum Intervention”:

- The main management objective is biodiversity

- Human intervention is limited to a minimum

- Activities other than listed below are prevented in the protected area: Ungulate/game control/Control of diseases/insect outbreaks, Public access, Fire intervention, Non-destructive research not detrimental to the management objective, Subsistence resource use (In case of expected large diseases/insect outbreaks control measures using biological methods are allowed provided that no other adequate control possibilities in buffer zones are feasible. Subsistence resource use to cover the needs of indigenous people and local communities, in so far as it will not adversely affect the objectives of management).

Class 1.3: “Conservation Through Active Management”

- The main management objective is biodiversity

- A management with active interventions directed to achieve the specific conservation goal of the protected area is taking place

- Any resource extraction, harvesting, silvicultural measures detrimental to the management objective as well as other activities negatively affecting the conservation goal are prevented in the protected area

Class 2: Main Management Objective "Protection of Landscapes and Specific

Natural Elements"

- Interventions are clearly directed to achieve the management goals landscape diversity, cultural, aesthetic, spiritual and historical values, recreation, specific natural elements

- The use of forest resources is restricted

- A clear long-term commitment and an explicit designation as specific protection regime defining a limited area is existing

- Activities negatively affecting characteristics of landscapes or/and specific natural elements mentioned are prevented in the protected area

Unit of

measurement

% of FOWL area protected under each MCPFE classes: 1.1, 1.2, 1.3, 2.

Data source FOREST EUROPE, UNECE and FAO enquiry on pan-European quantitative indicators:

State of Europe's Forests Report

References/location

of the data

State of Europe's Forests Report (SoEF), 2011 prepared by Forest Europe, UNECE and

FAO, Indicator 4.9: Protected forests, Table A4.15: Area of forest and other wooded

land, according to MCPFE Assessment

Guidelines.(http://www.unece.org/forests/fr/outputs/soef2011.html).

Data collection level National (NUTS 0).

Frequency

Every 5 years (e.g.: 2010, 2015).

Delay 1 year

Comments/caveats

63

INDICATOR C.39

Indicator Name Water abstraction in agriculture

Type of indicator Environment

Definition

This indicator refers to the volume of water which is applied to soils for irrigation

purposes. Data concern water abstraction from total surface and ground water.

In addition, information on the share of water abstraction in agriculture (for irrigation

purposes) as a percentage of total gross (freshwater) abstraction can also be used to

complement the indicator.

Agriculture is a major user of water, primarily for irrigation in order to enhance the yield

and quality of crops. It is therefore an essential driving force in the management of water

use.

Volume of water which is applied to soil for irrigation:

- according to the definition applied in Council Regulation (EC) No 1166/2008 and in

Commission Regulation (EC) No 1200/2009 on farm structure surveys and the survey on

agricultural production methods, the volume of water used for irrigation per year is

defined as the volume of water that has been used for irrigation on the holding during the

12 months prior to the reference date of the survey, regardless of the source (VIII.

Irrigation, Annex II of Commission Regulation (EC) No 1200/2009).

For each holding surveyed, Member States shall provide an estimation of the volume of

water used for irrigation on the holding in cubic metres. The estimation may be produced

by means of a model (art. 11 of Council Regulation (EC) No 1166/2008).

Share of irrigation in total water abstraction:

- according to the definitions delineated in the OECD/Eurostat Joint Questionnaire on

Inland Waters, "total gross abstraction for agriculture of which irrigation" is defined as the

"water which is applied to soils in order to increase their moisture content and to provide

for normal plant growth". The indicator shows the importance of irrigation in the volumes

of total water abstracted by countries for different sectors of water use.

Unit of

measurement m³

Data source

1) Eurostat – Survey on Agricultural Production Methods (SAPM): once-only survey carried

out in 2010. Estimations of the volume of water used for irrigation were collected. The

availability of this data source in the future is uncertain.

2) Eurostat via the Joint OECD/Eurostat Questionnaire, Section Inland Water; data on

water abstraction by agriculture for irrigation purposes are provided voluntarily by

Member States.

References/location

of the data

Location of the data:

1) Eurostat – statistics on the structure of agricultural holdings - SAPM 2010 – Table:

Irrigation - number of farms, areas and equipment by size of irrigated area and NUTS 2

regions (ef_poirrig), data: volume of water used for irrigation per year, m³.

2) Eurostat – environment statistics - Table annual water abstraction by source and by

64

sector (env_wat_abs), data water abstraction for irrigation purposes. Information on the

share of water abstraction in agriculture (for irrigation purposes) as a percentage of the

total gross (freshwater) abstraction is also available.

References

- Commission Regulation (EC) No 1200/2009. Implementing Regulation (EC) No 1166/2008 on Farm Structure Surveys (FSS) and SAPM, as regards livestock unit coefficients and definitions of the characteristics;

- OECD/Eurostat Joint Questionnaire on inland waters – Metadata;

- Agro-environmental indicator (AEI) 20: Water abstraction, as defined in the COM (2006)

508 on "Development of agri-environmental indicators for monitoring the integration of

environmental concerns into the CAP". http://ec.europa.eu/eurostat/statistics-

explained/index.php/Agri-environmental_indicator_-_water_abstraction

Data collection level

1) National (NUTS 0) and regional level (NUTS 2) (Eurostat –SAPM, 2010).

2) National (OECD/Eurostat Joint Questionnaire) and regional level (NUTS 2) (Eurostat –

voluntary questionnaire to Member States, water abstraction by NUTS 2 regions. Quality of

data at regional level is quite poor at the moment, but the situation should improve in the

future).

Frequency

1) The SAPM is a one-off survey undertaken in 2010.

2) Annual data (e.g., for the time being data are available for the period 1970-2011

depending on availability for each Member State. In 2009, 2010, 2011 data are available

for 15, 16, 11 Member States respectively from the Eurostat/OECD Joint Questionnaire).

Delay

1) 2-3 years (Eurostat, Survey on Agricultural Production Methods)

2) In general, the times lag between the period covered by the data and publication

amounts to 12-24 months (OECD/Eurostat Joint Questionnaire).

Comments

/caveats

The most appropriate source so far is the SAPM. Data are available for all Member States,

the survey is specific for the agricultural sector and data are more complete both at

regional and national level. However, data from the SAPM are available only for 2010.

Whether or not the survey is repeated in the future is currently under discussion.

Several Member States set up models for estimating the volume of water used in

agriculture for the SAPM (to avoid burden to farmers who alternatively had to report

directly the volume of water used). Therefore, it would be also worthwhile to further study

these models and verify whether they could be used annually to estimate the water

abstraction for irrigation, on the basis of FSS data, annual crop statistics and

meteorological data.

The quality of information collected via the Eurostat/OECD Joint Questionnaire is expected

to improve in the future. From this source, information on the share of water abstraction

in agriculture (for irrigation purposes) as a percentage of the total gross water abstraction

is also available; it would also allow comparing the use of water in different sectors.

A questionnaire on water quantities (including water used for irrigation) at NUTS 2 level

has also been established by Eurostat; the quality of data at the moment is quite poor but

improvements are expected in the future.

65

INDICATOR C.40

Indicator Name Water quality

Type of indicator Environmental

Definition

The water quality indicator shows the potential impact of agriculture on water

quality due to pollution by nitrates and phosphates.

Pollution by nitrates and phosphates is assessed through two main indicators,

each of which is composed of two sub-indicators:

Indicator 1) Gross Nutrient Balance which comprises:

1.a) Gross Nitrogen Balance (GNB-N): Potential surplus of nitrogen on agricultural

land (Gross Nitrogen Surplus).

1.b) Gross Phosphorus Balance (GNB-P): Potential surplus of phosphorus on

agricultural land (Gross Phosphorus Surplus).

The gross nutrient balances provide an estimate of the potential water pollution.

They represent the total potential threat of nitrogen and phosphorus surplus in

agricultural soils to the environment. When N and P are applied in excess, they

can cause surface and groundwater (including drinking water) pollution and

eutrophication.

Indicator 2) Nitrates in freshwater which consists of:

2.a) Groundwater quality: % of monitoring sites in 3 water quality classes (high,

moderate and poor);

2.b) Surface water quality: % of monitoring sites in 3 water quality classes (high,

moderate and poor).

The three water quality classes are defined as follows:

- High quality: concentration close to natural values or within the threshold

indicated in the legislation for low-polluted water.

- Moderate quality: concentration above natural standard but still below

hazardous level.

- Poor quality: concentration above hazardous level.

The actual concentration classes are the following.

Groundwater

- High ("<10 mg/l NO3 " + ">=10 mg/l NO3 and <25 mg/l NO3 ")10

- Moderate (">=25 mg/l NO3 and <50 mg/l NO3 ")

10

Although the natural concentration of NO3 in groundwater is below 10 mg/l, in the Nitrate Directive for water bodies that show concentrations below 25 mg/l the monitoring programme should be repeated every eight years instead of four, in this line this threshold can be taken into account to design high quality or low-polluted water bodies.

66

- Poor (">=50 mg/l NO3 ").

Surface water

- High ("<0.8 mg/l N " + ">=0.8 mg/l N and <2.0 mg/l N ")11

- Moderate (">=2.0 mg/l N and <3.6 mg/l N " + ">=3.6 mg/l N and >5.6 mg/l

N ")

- Poor (">=5.6 mg/l N and <11.3 mg/l N " + ">=11.3 mg/l N ")

The following indicators already exist:

- Agri-environmental indicator 27.1 Water quality – Nitrates in freshwater: nitrate

pollution of water. http://ec.europa.eu/eurostat/statistics-

explained/index.php/Agri-environmental_indicator_-_nitrate_pollution_of_water

- CSI 020 Nutrients in freshwater (European Environment Agency). Concentrations

of nitrate in rivers and groundwater. The indicator can be used to illustrate

geographical variations in current nutrient concentrations and temporal trends.

- Agri-environmental indicator 15 Gross Nitrogen Balance: Potential surplus of

nitrogen on agricultural land, http://ec.europa.eu/eurostat/statistics-

explained/index.php/Agri-environmental_indicator_-_gross_nitrogen_balance

- Agri-environmental indicator 16 Risk of pollution by phosphorus (Gross

Phosphorus Balance): Potential surplus of phosphorus on agricultural land,

http://ec.europa.eu/eurostat/statistics-explained/index.php/Agri-

environmental_indicator_-_risk_of_pollution_by_phosphorus

Unit of measurement

1) Gross Nutrient Balance

1.a) Gross Nitrogen Balance (GNB-N): kg N/ ha/ year;

1.b) Gross Phosphorus Balance (GNB-P): kg P/ ha/ year.

The indicator is expressed as a 4 year average

2) Nitrates in freshwater

2.a) Groundwater quality: % of monitoring sites;

2.b) Surface water quality: % of monitoring sites.

N.B. The concentration of nitrate is expressed as mg/l of nitrates (NO3-mg/l) for groundwater and mg/l of nitrogen (N-mg/l) for rivers.

Data source

1) Gross Nutrient Balance:

- Eurostat, Agri-environmental indicators (AEIs)

2) Nitrates in freshwater

- European Environment Agency (EEA) – Nutrients in freshwater: Data voluntarily

reported by Member States (EEA Member Countries) via the WISE/SOE (State of

Environment) data flow annually.

11

While natural concentration of nitrates in freshwater is about 1 mg/l, concentrations over 10 mg/l (2 mg-N/l) are those at which eutrophication and other negative effects on aquatic ecosystems appear. Therefore, this limit could be taken into account to design high quality or low-polluted water bodies.

67

References/location of

the data

1) Gross Nutrient Balance:

- Eurostat, Agri-environmental indicators, Pressure and Risks, Table Gross Nutrient

Balance (aei_pr_gnb);

2) Nitrates in freshwater

- EEA website, based on data reported to EIONET: Waterbase_rivers,

Waterbase_groundwaters, CSI020 , http://www.eea.europa.eu/data-and-

maps/indicators/nutrients-in-freshwater;

References

- European Environment Agency (EEA): WISE-SoE Water Information System for

Europe – State of Environment

- Council Directive 91/676/EEC concerning the protection of waters against pollution by nitrates from agricultural sources.

Data collection level

1) Gross Nutrient Balance: national

2) Nitrates in freshwater

- data from the European Environment Agency: national and river basin

level/water body

- data from the Nitrate Directive reporting system (DG environment): national and

river basin level.

Frequency

1) Gross Nutrient Balance: variable (e.g., data are currently available for the

period 2001-2012. In the future reporting should be every 2 years)

2) Nitrates in freshwater

- data from the European Environment Agency: annual.

Delay

1) Gross Nutrient Balance: not defined.

2) Nitrates in freshwater: data from the European Environment Agency: data

available 1 ½ year later.

Comments/caveats

The AEI 15 on Gross Nutrient Balance is at the moment considered the most

appropriate indicator to assess the CAP's impact on water quality, since it is

directly linked with agriculture. It must be noted, however, that this indicator is

only indirect; it shows the potential risks, depending on local soil conditions and

farm management practices, rather than the actual water quality trends.

For the interpretation of nitrates in fresh water, it should be kept in mind that it is

hardly feasible to distinguish the contribution of agriculture or the role of a policy

to this status compared to other influencing factors, even though it is

acknowledged that agriculture is a main contributor.

For this reason the preferred option is to use data for Gross Nutrient Balance (4-

year average) in combination with data for nitrates in freshwater by water quality

classes. On the one hand, figures for nitrates in freshwater would give a

comprehensive overview of the actual state of water bodies, allowing comparison

over time. On the other hand, data for Gross Nutrient Balance would provide an

68

indication of the impact of agriculture on those figures and give information about

potential pollution by phosphates.

Since data for both indicators are only available at national level and since annual

national balances can mask important regional or monthly variations, other

sources at Member State level should be explored. As for the GNB, in the future,

data should also be available at regional level (NUTS 2). Eurostat and the Joint

Research Centre are working on a pilot project with 5 countries to regionalise GNB

data.

Data on pesticides are currently less robust than those for nitrates, thus the

originally proposed component on pesticides in freshwater has been dropped

from the indicators for water quality.

69

INDICATOR C.41

Indicator Name Soil organic matter in arable land

Type of indicator Environment

Definition

The indicator estimates the total organic carbon content in arable soils.

Soil organic carbon, the major component of soil organic matter, is extremely

important in all soil processes. Organic matter in the soil is essentially derived from

residual plant tissues, while microbial, fungal and animal contributions constitute a

small part of its total amount. Microbes, fungi and animals decompose organic matter

more or less efficiently depending on temperature, moisture and ambient soil

conditions. The annual rate of loss of organic matter can vary greatly, depending on

cultivation practices, the type of plant/crop cover, drainage status of the soil and

weather conditions. There are two groups of factors that influence inherent organic

matter content: natural factors (climate, soil parent material, land cover and/or

vegetation and topography), and human-induced factors (land use, management and

degradation) (de Brogniez, D., Ballabio, C., Stevens, A., Jones, R. J. A., Montanarella, L.

and van Wesemael, B. (2014), A map of the topsoil organic carbon content of Europe

generated by a generalized additive model. European Journal of Soil Science.)

The indicator is expressed as an estimate of the total Soil Organic Carbon stocks in topsoil (0-20) of EU Member States.

Also the mean Soil Organic Carbon concentration per Member State is calculated,

though solely for orientation purposes since it has very limited scientific meaning

given the high variability of Soil Organic Carbon concentration in different areas.

The following indicators on soil quality also exist:

- Agro-environmental indicator (AEI 26) Soil Quality:

http://ec.europa.eu/eurostat/statistics-explained/index.php/Agri-

environmental_indicator_-_soil_quality

Methodology:

The indicator is based on the map of topsoil organic carbon content at the European

scale elaborated by the Joint Research Centre of the European Commission. The map

is based on estimates calculated by applying digital soil mapping techniques to the

first European harmonized geo-references topsoil (0-20 cm) database, which arises

from the Land Use/Cover Area frame statistical Survey(LUCAS), 2009.

LUCAS is a field survey programme to monitor changes in the management and

nature of the land surface of the European Union. It is also used for the collection of

soil samples and their subsequent analysis to produce updated and harmonised maps

of relevant soil parameters, including topsoil organic carbon (0-20 cm). In 2009 19 969

soil samples were collected in 25 Member States (EU-27 except Bulgaria and

Romania) and in 2012 ca. 2 000 soil samples in Bulgaria and Romania were added.

Data for Romania and Bulgaria are still awaited.

The map produced gives the most up-to-date general picture of topsoil organic

carbon content at the European Union scale and is not intended to be a substitute for

national scale or local maps that are based on more detailed spatial information.

70

Moreover, it is important that the uncertainty associated with the predicted values is

understood by the end-users and should encourage careful use and interpretation of

the spatial values. The maps produced in this study will be freely available for

download from the European Soil Data Centre website

http://eusoils.jrc.ec.europa.eu/

Unit of

measurement

Total Soil Organic Carbon (SOC) in arable land: megatonnes (Mt);

Mean SOC concentration in arable land: g/kg.

Data source

- Joint Research Centre (JRC Ispra) – Map of Topsoil Organic Carbon Content of

Europe based on Land use/cover Area frame statistical Survey (LUCAS), (current

version: 2009). The map is elaborated by the European Soil Database hosted by the

Joint Research Centre;

- de Brogniez, D., Ballabio, C., Stevens, A., Jones, R. J. A., Montanarella, L. and van

Wesemael, B. (2014), A map of the topsoil organic carbon content of Europe

generated by a generalized additive model. European Journal of Soil Science.

doi: 10.1111/ejss.12193;

Other sources: Potential sources available at national level (studies, surveys, reports),

models and estimation (e.g. AEIs).

References/location

of the data

The Map of Topsoil Organic Carbon Content is available on the European Soil

Datacentre hosted by the Joint Research Centre http://eusoils.jrc.ec.europa.eu/

Other sources: National studies, surveys, reports

Data collection level National (NUTS 0), regional (NUTS 2).

Frequency

The map is regularly updated depending on the availability of new data (e.g. the

current version of the map is based the 2009-2012 LUCAS soil survey results; the next

survey is foreseen in 2015).

LUCAS survey is in principle carried out every three years. If this frequency is

maintained in future, it could be envisaged that every second or third LUCAS survey

(i.e. every six to nine years) a soil module could be added to determine changes

compared to the 2009-2012 baseline.

Delay The expected delay between soil sampling and the publication of the results is about

two years.

Comments/caveats

The future of LUCAS survey: the survey, or certain components of it, might be

repeated as a monitoring exercise in the future. There is an on-going discussion on

the future of the LUCAS survey. In principle it should be repeated every 3 years. For

the long term, Eurostat is planning to integrate more and more national statistics and

the general results of LUCAS.

The indicator should be ideally complemented by an assessment of soil biodiversity.

The agri-environmental indicator (AEI) 26 - soil quality, elaborated by the Joint

Research Centre of the European Commission is not directly measurable since is

based on modelling and estimations are based on different sources and parameters.

It will not be updated regularly.

71

INDICATOR C.42

Indicator Name Soil erosion by water

Type of indicator Environment

Definition

This indicator consists of 2 sub-indicators:

1) Estimated rate of soil loss by water erosion;

2) Estimated agricultural area affected by a certain rate of soil erosion by water.

(The estimated area is also expressed as share of the total agricultural area).

The indicators assess the soil loss by water erosion processes (rain splash, sheetwash and rills) and give indications of the areas affected by a certain rate of soil erosion (moderate to severe, i.e. >11 t/ha/year in the OECD definition).

Estimates of soil loss by water erosion in Europe are expressed in t ha-1 year-1 for cells

of 100m x 100m for the EU.

The two soil erosion indicators have been produced by the Joint Research Center of the European Commission (JRC-Ispra), on the basis of an empirical computer model. Assessments of soil erosion are based on the output of an enhanced version of the Revised Universal Soil Loss Equation model (named RUSLE2015) (JRC-Ispra) which was developed to evaluate soil erosion by water at a regional scale. The model provides an estimate of possible erosion rates and estimates sediment delivery on the basis of accepted scientific knowledge, peer review published manuscripts, technical judgment and input datasets. In this assessment, the basic RUSLE model has been adapted through the improved quality of the input layers.

RUSLE2015 improves the quality of estimation by introducing updated (2010), high-resolution (100m) and peer-reviewed input layers of rainfall erosivity, soil erodibility, slope steepness and slope length, Land Cover and management and the support practices applied to control erosion. The Rainfall Erosivity was calculated based on high-resolution temporal rainfall data (5, 10, 15, 30 and 60 minutes) collected from 1,541 well-distributed precipitation stations across Europe. The Soil erodibility is estimated for the 20,000 field sampling points including in the Land Use/Cover Area frame (LUCAS) survey. The Land Cover and management accounts for the influence of land use (mainly vegetation type/cover and crop type) and management practices (mainly in arable lands) in reducing the rate of soil erosion by water. The Slope Steepness and Slope Length have been calculated using the latest Digital Elevation Model (DEM) at 25m. The support practices were estimated for the first time at European level taking into consideration the Good Agricultural and Environmental Conditions (GAEC).

Only soil erosion resulting from rains plash, overland flow (also known as sheetwash) and rill formation are considered. These are some of the most effective processes to detach and remove soil by water. In most situations, erosion by concentrated flow is the main agent of erosion by water.

The results of the soil erosion indicators have been aggregated at NUTS 3 and NUTS 2 level.

The rates of soil loss by water erosion (t ha-1 year-1) at Member State level represent national average values and therefore may mask higher erosion rates in many areas even for those countries that have a low mean.

The total area of agricultural land has been defined on the basis of Corine Land Cover (CLC) 2006 classes and includes the area of arable and permanent crops, pastures and permanent grasslands.

72

Estimated data on soil erosion are published following a qualitative assessment and compared with EIONET country estimates showing that the model output matches general erosion patterns across Europe. However also quantitative validation is foreseen to take place against long-term erosion plots.

The following indicators also exist:

- Agro-environmental indicator (AEI) 21 Soil Erosion,

http://ec.europa.eu/eurostat/statistics-explained/index.php/Agri-

environmental_indicator_-_soil_erosion

1) and 2) above are, respectively, the supporting and main indicator of the AEI 21.

- Soil erosion datasets of 9 European Union Countries have been collected through

the EIONET-SOIL network during 2010.

Unit of

measurement

1) t/ha /year

2) ha, %

Data source

- Joint Research Centre (JRC) – European Soil Data Centre (ESDAC)

- Input data sources used for the model: LUCAS Topsoil 2009, European Soil

Database, Corine Land Cover 2006, Rainfall Erosivity Database in Europe (REDES),

Copernicus Remote Sensing, Eurostat Statistics, Digital Elevation Model (DEM),

Good Agricultural Environmental Conditions (GAEC), Lucas Earth Observations 2012

- Potential sources available at national level (studies, surveys, reports) can be

explored and used.

References/location

of the data

- Joint Research Centre (JRC) – ISPRA, http://eusoils.jrc.ec.europa.eu/

- National studies, surveys, reports

Data collection level National (NUTS 0) and regional (NUTS 2-3) level (based on 1 km cell – model output).

Frequency

A new soil erosion dataset is being published by the JRC in 2015.

To evaluate changes in soil erosion over time it should be noted that an analysis over

a time period of at least 15-20 years would be necessary (e.g. comparing the current

situation to the 1990s). The time interval of 6 years (e.g. 2000-2006 for which data

are available) is limited and differences are primarily due to changes in land cover (as

indicated by Corine Land Cover data). Therefore, any conclusion must be drawn with

caution.

Delay Not defined.

Comments/caveats

The soil erosion indicator has been improved (e.g. taking into consideration the

impact of Good Agricultural Environmental Conditions - GAEC) to better measure the

link between agriculture and soil erosion. The new updated soil loss map takes into

account land management practices such as reduced tillage, the planting of cover

crops, keeping plant residues at the soil surface, the maintenance of stone walls, and

the increased use of grass margins and contour farming.

As it is now, the indicator can only give an indication of the erosion of soil in particular

contexts. The estimated erosion rates are linked to agricultural practices and

therefore the indicator reflects and captures the effects of policy measures to prevent

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erosion by agriculture. Moreover, the indicator gives only estimations and it is not

directly measurable since it is based on modelling and estimations from different

sources and parameters. It will not be updated regularly (depending on availability of

resources). The individual layers which have been used to produce the indicator have

been peer reviewed and accepted for publication by the scientific community. The

individual input layers are also available in the European Soil Data Centre (ESDAC).

In 2010, the European Soil Data Centre (ESDAC) invited the Primary Contact Points

(PCPs) of EIONET to contribute to a data collection campaign of EIONET-SOIL in order

to develop the European datasets for soil erosion and Soil Organic Carbon (SOC).

There was no legal obligation for the EIONET member countries to participate and

PCPs and NRCs for soil contributed on a voluntary basis.

18 EIONET countries did not reply or declared that they do not own the requested soil

data and/or refused to deliver data due to legal issues or other restrictions

Due to this fact some discrepancies could appear between the data collected at

Member State level and those presented by the JRC. The Member States that detect

such a discrepancy are strongly recommended to submit their data through EIONET in

order to allow the update and improvement of the model. The list of EIONET contact

points for SOIL is available at the following URL:

http://eusoils.jrc.ec.europa.eu/library/data/eionet/PrimaryPoints.cfm

The indicator only covers soil erosion by water. JRC has developed datasets for the

qualitative assessment of wind erosion. The quantitative assessment will be

concluded by the end of 2015.

http://eusoils.jrc.ec.europa.eu/library/themes/erosion/winderosion/

References

Panagos, P., Meusburger, K., Ballabio, C., Borrelli, P., Alewell, C. (2014) Soil erodibility

in Europe: A high-resolution dataset based on LUCAS. Science of Total Environment,

479–480 (2014) pp. 189–200

Panagos, P., Ballabio, C., Borrelli, P., Meusburger, K., Klik, A., Rousseva, S., Tadic, M.P.,

Michaelides, S., Hrabalíková, M., Olsen, P., Aalto, J., Lakatos, M., Rymszewicz, A.,

Dumitrescu, A., Beguería, S., Alewell, C. Rainfall erosivity in Europe. Sci Total Environ.

511 (2015), pp. 801-814.

Panagos, P., Borrelli, P., Meusburger, K., van der Zanden, E.H., Poesen, J., Alewell, C.

2015. Modelling the effect of support practices (P-factor) on the reduction of soil

erosion by water at European Scale. Environmental Science & Policy 51: 23-34

Panagos, P., Borrelli, P., Meusburger, K. 2015. A New European Slope Length and

Steepness Factor (LS-Factor) for Modelling Soil Erosion by Water. Geosciences, 5: 117-

126

Panagos, P., Borrelli, P., Meusburger, C., Alewell, C., Lugato, E., Montanarella, L.,

2015b. Estimating the soil erosion cover management factor at European scale. Land

Use Policy Journal (in Revision).

Panagos, P., Meusburger, K., Van Liedekerke, M., Alewell, C., Hiederer, R.,

Montanarella, L. 2014. Assessing soil erosion in Europe based on data collected

through a European Network. Soil Science and Plant Nutrition, 2014, Vol. 60 (1), pp.

15-29

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INDICATOR C.43

Indicator Name Production of renewable energy from agriculture and forestry

Type of indicator Environment

Definition

This indicator measures the production of renewable energy from agriculture and forestry and the share of this in the total production of renewable energy

It is broken down by sector:

Production of renewable energy from agriculture

Production of renewable energy from forestry

Due to data availability issues, production of renewable energy from agriculture covers:

Biodiesel from oilseeds crops

Ethanol from starch/sugar crops

Energy from agricultural biogas (livestock manure and energy crops, waste and residues)

It does not cover:

Other renewable energy from agriculture, like heat from cereal straw etc.

Part of the EU biodiesel production is based on non-domestic sources (imported vegetable oils, oilseeds), therefore an ad-hoc quantification of domestic production is not possible. In addition, the category "energy from agricultural biogas", even though it predominantly covers agricultural biogas, also contains some biogas from municipal solid waste etc.

Production of renewable energy from forestry covers:

Purpose-grown energy crops (poplar, willow, etc.)

Woody material generated by an industrial process (wood/paper industry in particular) or provided directly by forestry and agriculture (firewood, wood chips, bark, sawdust, shavings, chips, black liquor etc.)

Wastes such as straw, rice husks, nut shells, poultry litter, crushed grape dregs etc.

Production of renewable energy from agriculture:

The total production of renewable energy from agriculture is the sum of biodiesel,

bioethanol and biogas production, all expressed in ktoe (kilotons of oil

equivalent).

Primary data on biodiesel, expressed in kilotons, are converted into ktoe by

applying the coefficient defined by Directive 2009/28/EC

(1 tonne of biodiesel = 0.8837 tons of oil equivalent)

Primary data on bioethanol, expressed in 1000 litres, are converted into ktoe by

applying the coefficient defined by Directive 2009/28/EC

(1000 litres of bioethanol = 0.5016 tons of oil equivalent).

Production of renewable energy from forestry: No calculation needed. Data on

renewable energy from Wood & Wood Waste are directly available from the Eurostat

75

website.

Unit of

measurement

kilotonnes (1000 tonnes of oil equivalent, ktoe) and

% of total production of renewable energy

Data source EurObservER, EBB & ePURE.

Eurostat - Energy statistics

References/location

of the data

Renewable energy from agriculture:

DG Agriculture and Rural Development estimates based on:

Data on biogas: EurObserv'ER, The State of renewable energy in Europe "Primary production of biogas in the European Union" (Other biogas: decentralised agricultural plants, municipal solid waste methanisation plants, centralised plants), available on http://www.eurobserv-er.org/

Data on biodiesel: European Biodiesel Board (EBB) website http://www.ebb-eu.org/stats.php - TABLE "Production by country". Also on request to Dermot Buttler db@ebb-eu.org

Data on bioethanol provided by ePURE – European Renewable Ethanol http://www.epure.org

Renewable energy from forestry:

Eurostat – Energy Statistics

TABLE ngr_107a - PRODUCT Solid biofuels (excluding charcoal) - INDIC_NRG primary production

Total production of renewable energy:

Eurostat – Energy Statistics

TABLE nrg_100a - PRODUCT renewable energies - INDIC_NRG primary production

total

Total energy production:

Eurostat – Energy Statistics

TABLE nrg_100a- PRODUCT all products - INDIC_NRG primary production

Data collection level National (NUTS 0).

Frequency

Annual

Delay 2 years

Comments/caveats

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INDICATOR C.44

Indicator Name Energy use in agriculture, forestry and food industry

Type of indicator Environment

Definition

This indicator measures the direct use of energy in agriculture, forestry and food processing

The indicator is expressed in various ways: in kilotons, as a share of total final energy consumption and for the direct use of energy in agriculture and forestry as kg of oil equivalent per ha of UAA and forest land.

This indicator is based on Eurostat data from the joint IEA/OECD-Eurostat-UNECE questionnaires. There are some limitations of data and sources:

Although energy statistics are of high quality in general, the data on energy

consumption by agriculture are of lower quality due to errors and incomplete

data.

The indicator only refers to direct use of energy by agriculture. Indirect

energy used in agriculture for fertilisers, pesticides, animal feed and

agricultural machinery, which are produced using large amounts of energy, is

not included.

Data on energy consumption by agriculture from the questionnaires include

the use of energy by forestry. Though data on energy consumption by

fisheries should be reported separately since 2004, many data on

agriculture/forestry, even after 2004, include (part of) energy consumption by

fisheries. Energy consumption by agriculture may therefore be overestimated

in countries with significant forestry or fisheries sectors.

Data on food processing are taken from the category "Food and tobacco" and

therefore include the NACE category "Manufacture of food products,

beverages and tobacco products". Data on food processing are thus

overestimated.

Unit of

measurement

- total in kilotons (1000 tonnes) of oil equivalent, ktoe

For agriculture and forestry:

- kg of oil equivalent per ha of UAA

Data source Eurostat.

References/location

of the data

Direct use of energy in agriculture and forestry: Eurostat – Energy Statistics

TABLE ngr_100a - PRODUCT all products - INDIC_NRG Agriculture/Forestry

Direct use of energy in food processing: Eurostat – Energy Statistics

TABLE nrg_100a - PRODUCT all products - INDIC_NRG Food and tobacco

Final Energy Consumption: Eurostat – Energy Statistics

TABLE nrg_100a - PRODUCT all products - INDIC_NRG Final energy consumption

77

UAA: Eurostat

TABLE [apro_acs_a] Crop statistics (from 2000 onwards)

Forest area: Eurostat

TABLE for_area.-. INDIC_FO Forests

Data collection level National (NUTS 0).

Frequency

Every year for energy statistics. Every 5 years for forest area (e.g. 2010, 2015).

Every year for UAA crop statistics.

Delay 2 years

Comments/caveats Germany has not reported data on direct use of energy in agriculture/forestry.

78

INDICATOR C.45

Indicator Name Emissions from agriculture

Type of indicator Environment

Definition

This indicator is composed of two sub-indicators, one assessing greenhouse gas

(GHG) emissions and one ammonia emissions.

Indicator 1) GHG emissions from agriculture

The indicator measures net GHG emissions from agriculture including

agricultural soils:

1. Aggregated annual emissions of methane (CH4) and nitrous oxide (N2O) from

agriculture reported by Member States under the 'Agriculture' sector of the

national greenhouse gas inventory submitted to the United Nations Framework

Convention on Climate Change (UNFCCC Sector 4).

That sector includes the following sources of GHG from agriculture

i) enteric fermentation of ruminants (CH4);

ii) manure management (CH4, N2O);

iii) rice cultivation (CH4);

iv) agricultural soil management (mainly CH4, N2O).

2. Aggregated annual emissions and removals of carbon dioxide (CO2), and (where

these are not reported under the agriculture inventory) emissions of methane

(CH4) and nitrous oxide (N2O) from agricultural land uses (grassland and

cropland), are reported by Member States under the ‘Land Use, Land Use Change

and Forestry’ (LULUCF) sector of the national GHG inventory to the UNFCCC

(Sectors 5.A.B and 5.A.C).

Emissions of CO2 from the energy use of agricultural machinery, buildings and

farm operations, which are included in the ‘energy’ inventory under UNFCCC, are

not included in this indicator.

The indicator is a further development of the agri-environmental indicator (AEI)

19, 'Greenhouse Gas Emissions from Agriculture', which, however, only covers

CH4 and N2O from agricultural activities.

Indicator 2) Ammonia emissions from agriculture

This indicator measures total annual ammonia emissions (NH3) from agriculture,

also broken down by subcategory as follows:

- Synthetic N-fertilizers (NFR* subsector 3 D 1 a)

- Cattle dairy (NFR subsector 3 B 1 a)

79

- Cattle non-dairy (NFR subsector 3 B 1 b)

- Swine (NFR subsector 3 B 8)

- Laying hens (NFR subsector 3 B 9 a)

- Broilers (NFR subsector 3 B 9 b)

- All other subsectors (NFR subsectors 3 B 2-7 (except 3 B 5) + 3 B 9 c, d + 3 B 13 +

3 D 2 a, b, c + 3 F + 3 G + 3 B 13)

- Total agri emissions of NH3 (NFR subsectors 3 B 1-9 [except 3 B 5] + 3 B 13 + 3 D

1 a + 3 D 2 a, b, c + 3 F + 3 G)

(*NFR means National Format for Reporting, in accordance with the reporting

categories under the UNECE CLRTAP (Convention on Long Range Transboundary

Air Pollution and the National Emission Ceilings Directive, 2001/81 EC)

Unit of measurement

1) GHG emissions from agriculture

Absolute net GHG emissions are reported in tonnes of CO2 equivalents. Relative

net emissions are reported as a percentage of the net emissions in the reference

year 1990.

All GHGs are accounted on the basis of their global warming potential (GWP)

over a 100 year time period. GWP values are taken from IPCC (2007): CO2 = 1;

CH4 = 25; N2O = 298.

2) Ammonia emissions from agriculture

Kilotons of NH3

Data source

1) GHG emissions from agriculture

Annual European Union GHG inventory: The European Union, as a party to the

UNFCCC, reports annually on GHG inventories for the years 1990 to (t-2) for

emissions and removals within the area covered by its Member States (i.e.

domestic emissions taking place within its territory). The inventory is based on

national submissions to the UNFCCC and to the EU Monitoring Mechanism of CO2

and other GHG emissions. It is compiled and held by the European Environment

Agency (EEA) and the European Topic Centre on Air and Climate Change

(ETC/ACC).

Member States calculate sectoral emissions using standard methodologies (2006

guidelines of the Intergovernmental Panel on Climate Change - IPCC) according to

a common reporting framework agreed under the UNFCCC.

2) Ammonia emissions from agriculture

The European Environment Agency

Data are available through the existing reporting requirements under the National

Emission Ceilings Directive (2001/81 EC)

References/location of

the data

1) GHG emissions from agriculture

Annual EU GHG inventory (e.g. 2014 edition:

80

http://www.eea.europa.eu//publications/european-union-greenhouse-gas-

inventory-2014)

- CH4 and N2O emissions from agriculture are provided in Annex 2.8 CRF Tables

Agriculture.zip which includes standard reporting table (SRT) for sector 4

(agriculture).

- CO2 emissions from agricultural soils are recorded in Annex 2.9 CRF Tables

LULUCF.zip — European Environment Agency (EEA), which includes standard

reporting table (SRT) for sector 5 (LULUCF). Only categories 5B (cropland) and 5C

(grassland) are included. These account for emissions of cropland/grassland that

remain in the same type of land use, and emissions from land converted to

cropland/grassland.

The full set of data on GHG emissions and removals, sent by countries to the

UNFCCC and the EU GHG Monitoring Mechanism (EU Member States) is available

at the following EEA webpage: http://www.eea.europa.eu/data-and-

maps/data/national-emissions-reported-to-the-unfccc-and-to-the-eu-

greenhouse-gas-monitoring-mechanism-12

The web-based tool EEA GHG viewer provides access and analysis of the data

contained in the annual EU's GHG inventories since 1990. The EEA GHG data

viewer shows emission trends for the main sectors/categories and allows for

comparisons of emissions between different countries and activities. This data set

can be consulted at : http://www.eea.europa.eu/data-and-maps/data/data-

viewers/greenhouse-gases-viewer

2) Ammonia emissions from agriculture

The full set of data on emissions of air pollutants (NH3) from agriculture reported

annually by Member States to the European Commission (with copies to the EEA)

under Directive 2001/81/EC of the European Parliament and of the Council on

National Emission Ceilings for certain pollutants is available at the following EEA

webpage: http://www.eea.europa.eu/data-and-maps/data/national-emission-

ceilings-nec-directive-inventory-10

Annual emission data on ammonia emissions from agriculture, broken down by

Member State and sub-category is also provided through the web-based tool "Air

pollutant emissions data viewer (NEC Directive)". It also shows overall ammonia

emission trends over time, and allows for comparisons between Member States.

The link is:

http://www.eea.europa.eu/data-and-maps/data/data-viewers/emissions-nec-

directive-viewer

Data collection level Member State

Frequency Data collected annually

Delay

1) GHG emissions from agriculture

Year Y in June Y+2

81

2) Ammonia emissions from agriculture

One year (year Y in December Y + 1)

Comments/caveats

1) GHG emissions from agriculture

IPCC guidance allows countries to report GHG emissions and removals according

to different tiers. For most agriculture and LULUCF emissions and removals, tier 1

is based on the use of activity data (e.g. agricultural production statistics) and

global emission factors. Tier 2 follows the same approach but applies nationally

defined emission factors. Tier 3 involves the use of models and higher order

inventory data tailored to national circumstances. Methodologies for GHG

emission estimates should follow IPCC guidance, but need not be identical across

Member States.

In particular, when using lower tiers, GHG emission estimates do not capture the

effects of all mitigation measures that are supported by the CAP. That would

require a high level of stratification of activity data, and corresponding

information on emission factors, which often is not available. As a result, GHG

emission estimates, in particular in the 'agriculture sector' (non-CO2 gases) may

not reflect the impact of all measures put in place and have a high level of

uncertainty. However, the bulk of emissions and removals is captured by low-tier

methods. For example, the bulk of emissions in relation to agricultural soils is

caused by the cultivation of organic soils and the conversion of grasslands, which

can be represented by activity data.

This indicator differs from the Pillar I result indicator as it includes both

agricultural non-CO2 GHG emissions and emissions/removals from agricultural

soils. This more comprehensive approach is followed as instruments under Pillar I

and II address emissions/removals of both categories.

Member States are encouraged to improve GHG inventories towards higher tiers,

which would allow demonstrating the effects of technological improvements.

It is recognised that data constraints limit the level of information in some

Member States for this indicator. However, the situation should improve over

time as inventories become better developed.

2) Ammonia emissions from agriculture

Collection of these data is required under an existing reporting regime in the

National Emission Ceilings Directive (2001/81 EC) and will not add any additional

administrative burden for Member States.